US8265348B2 - Digital image acquisition control and correction method and apparatus - Google Patents

Digital image acquisition control and correction method and apparatus Download PDF

Info

Publication number
US8265348B2
US8265348B2 US13/191,239 US201113191239A US8265348B2 US 8265348 B2 US8265348 B2 US 8265348B2 US 201113191239 A US201113191239 A US 201113191239A US 8265348 B2 US8265348 B2 US 8265348B2
Authority
US
United States
Prior art keywords
eye
image
determining
frowning
mouth
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Active
Application number
US13/191,239
Other versions
US20110279700A1 (en
Inventor
Eran Steinberg
Peter Corcoran
Petronel Bigioi
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Fotonation Ltd
Original Assignee
DigitalOptics Corp Europe Ltd
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by DigitalOptics Corp Europe Ltd filed Critical DigitalOptics Corp Europe Ltd
Priority to US13/191,239 priority Critical patent/US8265348B2/en
Assigned to TESSERA TECHNOLOGIES IRELAND LIMITED reassignment TESSERA TECHNOLOGIES IRELAND LIMITED ASSIGNMENT OF ASSIGNORS INTEREST (SEE DOCUMENT FOR DETAILS). Assignors: FOTONATION VISION LIMITED
Assigned to FOTONATION VISION LIMITED reassignment FOTONATION VISION LIMITED ASSIGNMENT OF ASSIGNORS INTEREST (SEE DOCUMENT FOR DETAILS). Assignors: BIGIOI, PETRONEL, CORCORAN, PETER, STEINBERG, ERAN
Publication of US20110279700A1 publication Critical patent/US20110279700A1/en
Assigned to DigitalOptics Corporation Europe Limited reassignment DigitalOptics Corporation Europe Limited CHANGE OF NAME (SEE DOCUMENT FOR DETAILS). Assignors: TESSERA TECHNOLOGIES IRELAND LIMITED
Application granted granted Critical
Publication of US8265348B2 publication Critical patent/US8265348B2/en
Assigned to FOTONATION LIMITED reassignment FOTONATION LIMITED CHANGE OF NAME (SEE DOCUMENT FOR DETAILS). Assignors: DigitalOptics Corporation Europe Limited
Assigned to BANK OF AMERICA, N.A., AS COLLATERAL AGENT reassignment BANK OF AMERICA, N.A., AS COLLATERAL AGENT SECURITY INTEREST (SEE DOCUMENT FOR DETAILS). Assignors: ADEIA GUIDES INC., ADEIA IMAGING LLC, ADEIA MEDIA HOLDINGS LLC, ADEIA MEDIA SOLUTIONS INC., ADEIA SEMICONDUCTOR ADVANCED TECHNOLOGIES INC., ADEIA SEMICONDUCTOR BONDING TECHNOLOGIES INC., ADEIA SEMICONDUCTOR INC., ADEIA SEMICONDUCTOR SOLUTIONS LLC, ADEIA SEMICONDUCTOR TECHNOLOGIES LLC, ADEIA SOLUTIONS LLC
Active legal-status Critical Current
Anticipated expiration legal-status Critical

Links

Images

Classifications

    • GPHYSICS
    • G06COMPUTING; CALCULATING OR COUNTING
    • G06VIMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V40/00Recognition of biometric, human-related or animal-related patterns in image or video data
    • G06V40/10Human or animal bodies, e.g. vehicle occupants or pedestrians; Body parts, e.g. hands
    • G06V40/16Human faces, e.g. facial parts, sketches or expressions
    • G06V40/174Facial expression recognition
    • G06V40/175Static expression
    • GPHYSICS
    • G06COMPUTING; CALCULATING OR COUNTING
    • G06VIMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V10/00Arrangements for image or video recognition or understanding
    • G06V10/98Detection or correction of errors, e.g. by rescanning the pattern or by human intervention; Evaluation of the quality of the acquired patterns
    • GPHYSICS
    • G06COMPUTING; CALCULATING OR COUNTING
    • G06VIMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V10/00Arrangements for image or video recognition or understanding
    • G06V10/98Detection or correction of errors, e.g. by rescanning the pattern or by human intervention; Evaluation of the quality of the acquired patterns
    • G06V10/993Evaluation of the quality of the acquired pattern
    • GPHYSICS
    • G06COMPUTING; CALCULATING OR COUNTING
    • G06VIMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V40/00Recognition of biometric, human-related or animal-related patterns in image or video data
    • G06V40/10Human or animal bodies, e.g. vehicle occupants or pedestrians; Body parts, e.g. hands
    • G06V40/16Human faces, e.g. facial parts, sketches or expressions
    • G06V40/161Detection; Localisation; Normalisation
    • HELECTRICITY
    • H04ELECTRIC COMMUNICATION TECHNIQUE
    • H04NPICTORIAL COMMUNICATION, e.g. TELEVISION
    • H04N23/00Cameras or camera modules comprising electronic image sensors; Control thereof
    • H04N23/60Control of cameras or camera modules
    • H04N23/61Control of cameras or camera modules based on recognised objects
    • H04N23/611Control of cameras or camera modules based on recognised objects where the recognised objects include parts of the human body
    • HELECTRICITY
    • H04ELECTRIC COMMUNICATION TECHNIQUE
    • H04NPICTORIAL COMMUNICATION, e.g. TELEVISION
    • H04N23/00Cameras or camera modules comprising electronic image sensors; Control thereof
    • H04N23/60Control of cameras or camera modules
    • H04N23/64Computer-aided capture of images, e.g. transfer from script file into camera, check of taken image quality, advice or proposal for image composition or decision on when to take image

Definitions

  • the present system sets a condition under which a picture will not be taken or will not be used or further processed after it has already been taken, and/or where an additional image or images will be taken to replace the unsatisfactory image.
  • another advantageous feature of a system in accordance with a preferred embodiment is that it can correct an acquired frown region with a user's mouth information from a preview or post-view image or another full resolution image.
  • the present system preferably uses preview images, which generally have lower resolution and may be processed more quickly.
  • the present system can also look for changes in facial features (e.g., of the eyes or mouth), between images as potentially triggering a disqualifying of a scene for an image capture.
  • the description herein generally refers to handling a scene wherein an object person is frowning.
  • the invention may be applied to other features, e.g., when a person is blinking, or when a person is unsatisfactorily gesturing, talking, eating, having bad hair, or otherwise disposed, or when another person is putting bunny ears on someone, or an animal or other person unexpectedly crosses between the camera and human subject, or the light changes unexpectedly, or the wind blows, or otherwise.
  • One or more or all of these disqualifying circumstances may be manually set and/or overridden.
  • Block 1130 depicts the automatic orientation of the image, a tool that can be implemented either in the camera as part of the acquisition post processing, or on a host software.
  • the software in the mouth or face detection stage including the functionality of FIG. 5 , blocks 1108 and 1118 , will mark the two faces or the two mouths or four eyes of the mother and son, e.g., the faces may be marked as estimations of ellipses 2100 and 2200 , respectively.
  • the software will determine the main axes of the two faces 2120 and 2220 , respectively as well as the secondary axis 2140 and 2240 .
  • FIG. 7 c describes the step of extracting the pertinent features of a face, which are usually highly detectable.
  • Such objects may include the eyes, 2140 , 2160 and 2240 , 2260 , and the lips, 2180 and 2280 , or the nose, eye brows, eye lids, features of the eyes, hair, forehead, chin, ears, etc.
  • the combination of the two eyes and the center of the lips creates a triangle 2300 which can be detected not only to determine the orientation of the face but also the rotation of the face relative to a facial shot.
  • there are other highly detectable portions of the image which can be labeled and used for orientation detection such as the nostrils, the eyebrows, the hair line, nose-bridge and the neck as the physical extension of the face, etc.
  • the eyes and lips are provided as an example of such facial features Based on the location of the eyes, if found, and the mouth, the image might ought to be rotated in a counter clockwise direction.

Abstract

An unsatisfactory scene is disqualified as an image acquisition control for a camera. An image is acquired. One or more mouth regions are determined. The mouth regions are analyzed to determined whether they are frowning, and if so, then the scene is disqualified as a candidate for a processed, permanent image while the mouth is completing the frowning.

Description

CROSS-REFERENCE TO RELATED APPLICATIONS
This application is a Continuation of U.S. patent application Ser. No. 12/851,333, filed Aug. 5, 2010, now U.S. Pat. No. 8,005,268; which is a Continuation of U.S. patent application Ser. No. 11/460,225, filed Jul. 26, 2006, now U.S. Pat. No. 7,804,983; which claims priority to U.S. provisional patent application No. 60/776,338, filed Feb. 24, 2006. This application is related to U.S. patent application Ser. No. 11/460,218, now U.S. Pat. Nos. 7,792,335; and 11/460, 227, now U.S. Pat. No. 7,551,754 both filed on Jul. 26, 2006. Each of these applications is hereby incorporated by reference.
1. FIELD OF THE INVENTION
The invention relates to digital image acquisition, and particularly to disqualifying a scene as a candidate for a processed, permanent image due to the presence of one or more unsatisfactory features, such as blinking eyes, frowning faces, or occlusion or shadowing of facial features or other key features.
2. DESCRIPTION OF THE RELATED ART
Cameras are becoming strong computation tools. In particular, FotoNation, Inc., assignee of the present application, has developed many advantageous face detection tools. Some of these are described at U.S. patent application Ser. Nos. 10/608,776, 10/608,810, 10/764,339, 10/919,226, 11/182,718, and 11/027,001, which are hereby incorporated by reference.
This differs from using a trigger to take a picture. This also differs from waiting for an event that may or may not happen (e.g. a smile). U.S. Pat. No. 6,301,440 discloses adjusting image capture parameters based on analysis of temporary images, and awaiting taking a picture until everyone in the temporary images is smiling. The camera must await a certain event that may or may not ever happen. It is many times not acceptable to make people wait for the camera to decide that a scene is optimal before taking a picture, and there is no description in the '440 patent that would alleviate such dilemma. The '440 patent also provides no guidance as to how to detect or determine certain features within a scene.
There are also security cameras that take pictures when a subject enters the view of the camera. These generally detect motion or abrupt changes in what is generally a stagnant scene.
SUMMARY OF THE INVENTION
A method is provided for disqualifying an unsatisfactory scene as an image acquisition control for a camera. An analysis of the content of the captured image determines whether the image should be acquired or discarded. One example includes human faces. It may be determined whether an image is unsatisfactory based on whether the mouth is configured with a frown or the eyes are closed, partially closed or closing down or moving up during a blinking process. Alternatively, other non-desirable or unsatisfactory expressions or actions such as covering one's face with a hand or other occluding or shadowing of a facial feature or other key feature of a scene, or rotating the head away from the camera, etc., may be detected.
A present image of a scene is acquired or captured including a face region. One or more groups of pixels is/are identified corresponding to a region of interest such as a mouth region or an eye region within the face region. It is determined, e.g., whether the mouth region is in a frowning configuration. If so, then the scene is disqualified as a candidate for a processed, permanent image while the mouth is continuing to be frowning.
The present image may include a preview image, and the disqualifying may include delaying full resolution capture of an image of the scene. The delaying may include ending the disqualifying after a predetermined wait time.
A preview image may be used. This can provide an indication of a region of interest (ROI) where the eyes may be in the captured image. This provides a fast search in the final image of the mouth or eyes based on spatial information provided from the analysis of preview images.
The delaying may include predicting when the frowning will stop and ending the disqualifying at approximately the predicted frown stop time. The predetermined complete blinking process duration may be programmed based on an average frowning duration. The estimating may be based on analyzing a temporal capture parameter of one or more previous preview images relative to that of the present preview image. The estimating may involve a determination as to whether the mouth that is frowning is moving toward smiling or deeper frowning in the present preview image, and a degree to which the mouth is frowning.
The method may include determining whether the mouth is frowning including determining a degree to which the mouth is frowning. The degree to which the mouth is frowning may be determined based on relatively analyzing the present preview image and one or more other preview images relatively acquired within less than a duration of a frowning period. The determining whether the mouth is frowning may include determining a degree of blurriness of one or both lips. It may be determined what configuration the lips have or whether teeth are showing, or a combination thereof. A color analysis of the mouth region may be performed and differentiating pixels corresponding to an open versus closed mouth tone. A shape analysis of the mouth may be performed and a shape of the lips determined and/or pixels differentiated as corresponding to open versus closed mouth, teeth showing, etc.
The present image may include a full resolution capture image. The disqualifying may include foregoing further processing of the present image. It may be determined whether the mouth is frowning including determining a degree to which the mouth is frowning. This may include relatively analyzing the present preview image and one or more other preview images relatively acquired within less than a predetermined frowning duration wait time. The determination of whether the mouth is frowning or how long the mouth will be frowning may be based on determining a degree of blurriness of one or both lips and/or how the configuration of the lips has changed from a succeeding preview image.
The method may include determining a portion of one or more mouth features that may be showing. A color analysis may be performed and pixels differentiated as corresponding to frowning versus non-frowning features or tones. A shape analysis of the mouth may be performed and pixels differentiated as corresponding to a frown contrasted with other configurations.
The present image may include a full resolution capture image. The method may include assembling a combination image including pixels from the present image and open-eye pixels corresponding to the eye that is blinking from a different image. The different image may include a preview image or a post-view image or another full resolution image. The different image may include a lower resolution than the present image, and the assembling may include upsampling the different image or downsampling the present image, or a combination thereof. The method may also include aligning the present image and the different image, including matching a satisfactory mouth region to a frowning mouth region in the present image.
The invention may also be implemented to disqualify images out of a selection of images that are part of a stream, such as a video stream.
A mouth region may be identified based on identifying a face region, and analyzing the face region to determine the mouth region therein.
A new image may be captured due to the disqualifying to replace the present image.
A pair of images may be captured and analyzed to determine that at least one of the pair of images includes no blinking.
The interval between multiple captures can be calculated to be longer than a single blink time.
A warning signal may be provided regarding the frowning so that the photographer will be made aware that he or she should take another picture.
The invention in its various alternatives, may address single or multiple faces in a single image, such as a group shot. A second mouth region of a second face may be identified within the scene. Additional face regions or other key features that may be non-facial within a scene may be identified. It may be determined whether the second mouth region is in a frowning configuration, or another key feature is unsatisfactory in its configuration or position. If so, then the method may include disqualifying the scene as a candidate for a processed, permanent image while the second mouth is frowning. Capturing or further processing may be disqualified for full resolution images until the mouth regions of each face region within the scene include no frowning mouths.
A further method is provided for automatically disqualifying an unsatisfactory scene as an image acquisition control of a camera. The method includes acquiring multiple preview images. Information is extracted from the multiple preview images. One or more changes is/are analyzed in the scene between individual images of the multiple temporary images. Based on the analyzing, it is determined whether one or more unsatisfactory features exist within the scene. The scene is disqualified as a candidate for a processed, permanent image while the one or more unsatisfactory features continue to exist.
The analyzing may include identifying one or more groups of pixels that correspond to a facial feature having an unsatisfactory configuration. The one or more groups of pixels may include a mouth group, and the unsatisfactory configuration may include a frowning configuration. A disqualifying interval may be determined during which no processed, permanent image is to be acquired.
One or more processor readable storage devices having processor readable code embodied thereon are also provided. The processor readable code is for programming one or more processors to perform a method of disqualifying an unsatisfactory scene as an image acquisition control for a camera, as set forth herein above or below. The processor may be embedded as part of the camera or external to the acquisition device. The acquisition device may be a hand held camera, a stationary camera, a video camera, a mobile phone equipped with a acquisition device, a hand held device equipped with a acquisition device, a kiosk booth, such as ones used for portraits, a dedicated portrait camera such as one used for security or identifications or generically, any image capture device.
BRIEF DESCRIPTION OF THE DRAWINGS
FIG. 1 illustrates a method for disqualifying a scene that includes a frowning mouth in accordance with a preferred embodiment.
FIG. 2 illustrates a method of predicting a frowning time interval in accordance with a preferred embodiment.
FIG. 3 illustrates a method of determining a degree to which a mouth is frowning in accordance with a preferred embodiment.
FIG. 4 a illustrates a method of determining whether to forego further processing of an image in accordance with a preferred embodiment.
FIG. 4 b illustrates a method of assembling a combination image in accordance with a preferred embodiment.
FIG. 5 illustrates a preferred embodiment of a workflow of correcting images based on finding mouths in the images.
FIG. 6 a illustrates a generic workflow of utilizing mouth information in an image to delay image acquisition in accordance with a preferred embodiment.
FIG. 6 b illustrates a generic workflow of utilizing face information in a single or a plurality of images to adjust the image rendering parameters prior to outputting the image in accordance with a preferred embodiment.
FIGS. 7 a-7 d illustrate face, eye or mouth detection, or combinations thereof, in accordance with one or more preferred embodiments.
FIG. 8 a illustrates a frown detection and correction method in accordance with one or more preferred embodiments.
FIG. 8 b describes an illustrative system in accordance with a preferred embodiment to determine whether a mouth is blinking in the camera as part of the acquisition process, and whether to capture, discard or store the image, or whether to substitute a non-frowning mouth for a frowning mouth region.
FIG. 9 illustrate an automatic focusing capability in the camera as part of the acquisition process based on the detection of a mouth in accordance with one or more preferred embodiments.
DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS
Systems and methods are described in accordance with preferred and alternative embodiments. These techniques provide enhanced functionality and improved usability, as well as avoiding missed shots. With them, a digital camera is able to decide when a subject's facial expression may be inappropriate, unsatisfactory or non-desirable. One example is blinking, and others include frowning, occlusions and shadowing. The capture device can either not take the picture, delay the acquisition for an appropriate duration, or immediately take another picture, or warn a user, or take steps to enhance the unsatisfactory image later, or combinations of these or other steps. The camera may delay taking another picture for a certain amount of time such as 0.1 to 0.3 seconds or for an average frowning interval, or until the frowning is determined to be over. The user could be warned before snapping a picture or after the picture has been taken that the subject's mouth was frowning.
A predictive system is provided that disqualifies images if mouths are frowning, e.g., having lips turned downward at the edges of the mouth. The system predicts when a picture cannot be taken, i.e., those times when a detected frowning process will be ongoing.
Disqualified images may be already captured and disqualified only in a post-capture filtering operation, either within the camera or on an external apparatus. The system may take multiple images to enhance the probability that one or more of the images will not be disqualified for including one or more frowning mouths. Such system is useful in the case of a group shot where the probability of one subject in the process of blinking increases as the number of subjects increase. The system, based on the number of faces in the image, can automatically determine the amount of images to be sequentially taken to provide a probability that at least one of the images will have no blinking eyes that is above a threshold amount, e.g., 50%, 60%, 67%, 70%, 75%, 80%, 90% or 95%.
An image may be generated as a combination of a present image, and a preview, post-view or other full resolution image. For example, the combination image may include a face region and some background imagery, wherein a mouth region, which is unsatisfactorily frowning in the present image, is replaced with a mouth region that is not frowning from the preview, post-view or other full resolution image. This feature may be combined with features presented in U.S. patent application Ser. No. 10/608,776, which is assigned to the same assignee as the present application and is hereby incorporated by reference. In the '776 application, a method of digital image processing using face detection is described. A group of pixels is identified that corresponds to a face within a digital image. A second group of pixels is identified that corresponds to another feature within the digital image. A re-compositioned image is determined including a new group of pixels for at least one of the face and the other feature.
The embodiments herein generally refer to a single face within a digital image or scene (e.g., prior to image capture or that may have already been digitally captured), and generally to “a mouth”. However, these descriptions can be extended to other features on a single face, and to more than a single face (group shot), and the camera can disqualify the scene if a certain number of one or two, three, four or more mouths are determined to be frowning, e.g., in a group shot including 20 people, it may be permissible to have one or two frowning mouths such that a threshold of three frowning mouths is set before the scene will be disqualified. The camera is able to perform the disqualifying and/or other operations, as described herein or otherwise, until a high percentage or all of the subjects have non-frowning mouths.
In one embodiment, the camera will take the picture right after the subject stops frowning. The present system can be used to disqualify an image having a subject whose mouth or lips are in a frowning configuration, and can take multiple images to prevent having no images that lack frowns. One of the images will likely have non-frowning mouths for each subject person, and the pictures can have a mixture of pixels combined into a single image with no mouths frowning. The camera may decide on the number of images to take based on the number of subjects in the image. The more people, the higher the likelihood of one person frowning or blinking, thus more images should be acquired. If it is acceptable for efficiency that a certain percentage of persons may be frowning or blinking in a large group shot, e.g., that is below a certain amount, e.g., 5%, then the number of images can be reduced. These threshold numbers and percentage tolerances can be selected by a camera product manufacturer, program developer, or user of a digital image acquisition apparatus. This information may be generated based on analysis of preview images. The preview image may also assist in determining the location of the eyes, so that the post processing analysis can be faster honing into the region of interest as determined by the preview analysis.
The present system sets a condition under which a picture will not be taken or will not be used or further processed after it has already been taken, and/or where an additional image or images will be taken to replace the unsatisfactory image. Thus, another advantageous feature of a system in accordance with a preferred embodiment is that it can correct an acquired frown region with a user's mouth information from a preview or post-view image or another full resolution image. The present system preferably uses preview images, which generally have lower resolution and may be processed more quickly. The present system can also look for changes in facial features (e.g., of the eyes or mouth), between images as potentially triggering a disqualifying of a scene for an image capture.
The description herein generally refers to handling a scene wherein an object person is frowning. However, the invention may be applied to other features, e.g., when a person is blinking, or when a person is unsatisfactorily gesturing, talking, eating, having bad hair, or otherwise disposed, or when another person is putting bunny ears on someone, or an animal or other person unexpectedly crosses between the camera and human subject, or the light changes unexpectedly, or the wind blows, or otherwise. One or more or all of these disqualifying circumstances may be manually set and/or overridden.
FIG. 1 illustrates a method for disqualifying a scene that includes a frowning mouth in accordance with a preferred embodiment. A present image of a scene including a face region is acquired at 110. Optionally, the face region is identified at 120, and the face region analyzed to determine a mouth region therein. One or more groups of pixels corresponding to a mouth region within the face region are identified at 130. It is determined whether the mouth region is in a frown configuration at 140. If the mouth is determined to be frowning at 140, then the scene is disqualified as a candidate for a processed, permanent image at 150. At this point, the process can simply stop or start again from the beginning, or a new image may be captured due to the disqualifying in order to replace the present image at 160. A warning signal may be provided regarding the frowning at 170. Full resolution capture of an image of the scene may be delayed at 180. As illustrated at FIGS. 4A and 4B, further processing of a present image may be stopped or a combination image may be assembled as a way of enhancing the disqualified image.
FIG. 2 illustrates a method of predicting when the frowning will end in accordance with a preferred embodiment. It is predicted when the frowning will end at 210, and the disqualifying interval will end at the predicted frowning stop time. The interval may be set at a predetermined wait time 220. This may be set from a knowledge of an average frown duration of a second, or two seconds, or half a second, or so, or in a range from approximately 0.2 to 2.0 seconds, or to 0.5, 0.8, 1.0, 1.2 or 1.5 seconds, however setting the wait time too long to ensure the frowning is complete disadvantageously permits a second frown to begin or simply makes everyone involved in taking the picture have to wait to too long for the disqualifying period to end. A more precise determination of the end of the frowning is desired.
A degree to which a mouth may be frowning is provided at 230. The process of FIG. 3 may follow. It may be determined at 270 whether the frowning mouth is moving, and if so, in what direction. A frowning stop time may be estimated at 280 based on analyzing a temporal capture parameter of one or more preview images relative to that of the present image.
A degree to which a mouth may be frowning is further provided at 310 of FIG. 3. Shape analysis 360 may be preferably performed to differentiate pixels corresponding to features of the upper or lower lips, or both, that are frowning from pixels corresponding to features of upper or lower lips turned downward, or both, features corresponding to mouths that are not frowning such as teeth, dimples, wrinkles, creases, gums, or tongue showing, or lips not turned downward at the edges, that would appear in a mouth region of a present scene. The present image is preferably analyzed at 330 relative to one or more other preview images acquired within less than a duration of a frowning period. A portion of a mouth feature that is showing may be determined at 340 to facilitate determining to what degree the mouth may be frowning. An optional determination of a degree of blurriness at 350 of one or both lips may facilitate a determination of lips speed for determining when the frowning may end. Color analysis 360 may also be performed to differentiate pixels corresponding to features of non-frowning mouths such as teeth, dimples, wrinkles, creases, gums, or tongue, from pixels corresponding to features of frowning mouths or lips that would appear in a mouth region of a present scene.
FIG. 4 a illustrates a method of determining whether to forego further processing of an image 410 in accordance with a preferred embodiment. In this case, determining a degree to which the mouth is frowning 420 is performed for a different purpose than to compute a frowning stop time. In this embodiment, a threshold degree of frowning of a mouth may be preset, e.g., such that when an image is analyzed according to 420, 430, 440, 450, 460, or 470, or combinations thereof, similar to any or a combination of 310-360 of FIG. 3, then if the mouth is frowning to at least the threshold degree or greater, then the scene is disqualified, because the mouth is frowning too much or is substantially frowning. This can correspond to a situation wherein a mouth is not frowning, or where a mouth is at the very start or very end of a frowning movement, such that the degree to which the mouth is not frowning is sufficient for keeping the image.
FIG. 4 b illustrates a method of assembling a combination image in accordance with a preferred embodiment. At 480, a combination image is assembled including pixels from a present image and non-frowning mouth pixels from a different image that correspond to the mouth that is frowning in the present image. The different image may be a preview or postview image 490. In this case, particularly if the preview or postview image has lower resolution than the present image, then at 500 the preview image may be upsampled or the postview image may be downsampled, or a combination thereof. The present image and the different image are preferably aligned at 510 to match the non-frowning mouth pixel region in the preview of postview image to the frowning mouth region in the present image.
FIG. 5 illustrates further embodiments. If one or more mouths are determined to be frowning in an image, then that image is preferably disqualified from being further processed in accordance with the following. Alternatively, the frowning determination 140 may be performed somewhere along the way, such as illustrated as an example in FIG. 5. An image may be opened by the application in block 1102. The software then determines whether mouths or faces, or both, are in the picture as described in block 1106. If no mouths or faces are detected, the software ceases to operate on the image and exits 1110. In what follows, only mouths will be generally referred to for efficiency, but either faces or mouths, or eyes, or combinations thereof, or even another facial feature or other non-facial predetermined scene feature, may be the object of particular operations (see FIGS. 1, 110, 120 and 130 and U.S. application Ser. No. 10/608,776, which is incorporated by reference).
The software may also offer a manual mode, where the user, in block 1116 may inform the software of the existence of mouths, and manually marks them in block 1118. The manual selection may be activated automatically if no mouths are found, 1116, or it may even be optionally activated after the automatic stage to let the user, via some user interface to either add more mouths to the automatic selection 1112 or even 1114, remove regions that are mistakenly 1110 identified by the automatic process 1118 as mouths. Additionally, the user may manually select an option that invokes the process as defined in 1106. This option is useful for cases where the user may manually decide that the image can be enhanced or corrected based on the detection of the mouths. Various ways that the mouths may be marked, whether automatically of manually, whether in the camera or by the applications, and whether the command to seek the mouths in the image is done manually or automatically, are all included in preferred embodiments herein. In a preferred embodiment, faces are first detected, and then mouth is detected within each face.
In an alternative embodiment, the mouth detection software may be activated inside the camera as part of the acquisition process, as described in Block 1104. In this scenario, the mouth detection portion 1106 may be implemented differently to support real time or near real time operation. Such implementation may include sub-sampling of the image, and weighted sampling to reduce the number of pixels on which the computations are performed. This embodiment is further described with reference to FIG. 6 a.
In an alternative embodiment, the eye detection can then also make use of information provided from preview images to determine the location of the eyes in preview, thus expediting the analysis being performed in a smaller region on the final image.
In an alternative embodiment, the mouth detection software may be activated inside the rendering device as part of the output process, as described in Block 1103. In this scenario, the mouth detection portion 1106 may be implemented either within the rendering device, using the captured image or using a single or plurality of preview images, or within an external driver to such device. This embodiment is further described with reference to FIG. 6 b.
After the mouths and/or faces or other features are tagged, or marked, whether manually as defined in 1118, or automatically 1106, the software is ready to operate on the image based on the information generated by the mouth-detection, face detection, or other feature-detection stage. The tools can be implemented as part of the acquisition, as part of the post-processing, or both. As previously averred to, frown determination may be performed at this point at 140 (see FIGS. 1-4 b and above). The image may be disqualified at 1119 if frowning is found, such that processing further processing, as known to one familiar in the art of digital photography is efficiently foregone.
Block 1120 describes panning and zooming into the mouths or faces. This tool can be part of the acquisition process to help track the mouths or faces or other features and create a pleasant composition, or as a post processing stage for either cropping an image or creating a slide show with the image, which includes movement.
Block 1130 depicts the automatic orientation of the image, a tool that can be implemented either in the camera as part of the acquisition post processing, or on a host software.
Block 1140 describes the way to color-correct the image based on the skin tones of the faces or mouth tones or other feature tones. This tool can be part of the automatic color transformations that occur in the camera when converting the image from the RAW sensor data form onto a known, e.g. RGB representation, or later in the host, as part of an image enhancement software. The various image enhancement operations may be global, affecting the entire image, such as rotation, and/or may be selective based on local criteria. For example, in a selective color or exposure correction as defined in block 1140, a preferred embodiment includes corrections done to the entire image, or only to the face or mouth regions in a spatially masked operation, or to specific exposure, which is a luminance masked operation. Note also that such masks may include varying strength, which correlates to varying degrees of applying a correction. This allows a local enhancement to better blend into the image.
Block 1150 describes the proposed composition such as cropping and zooming of an image to create a more pleasing composition. This tool 1150 is different from the one described in block 1120 where the mouths or faces are anchors for either tracking the subject or providing camera movement based on the face location.
Block 1160 describes the digital-fill-flash simulation which can be done in the camera or as a post processing stage. Alternatively to the digital fill flash, this tool may also be an actual flash sensor to determine if a fill flash is needed in the overall exposure as described in Block 1170. In this case, after determining the overall exposure of the image, if the detected faces in the image are in the shadow, a fill flash will automatically be used. Note that the exact power of the fill flash, which should not necessarily be the maximum power of the flash, may be calculated based on the exposure difference between the overall image and the faces. Such calculation is well known to the one skilled in the art and is based on a tradeoff between aperture, exposure time, gain and flash power.
Block 1180 describes the ability of the camera to focus on the mouths or faces or other features. This can be used as a pre-acquisition focusing tool in the camera.
Referring to FIG. 6 a, which describes a process of using face detection to improve in camera acquisition parameters, as aforementioned in FIG. 5, block 1106. In this scenario, a camera is activated at 1000, for example by means of half pressing the shutter, turning on the camera, etc. The camera then goes through the normal pre-acquisition stage to determine at 1004 the correct acquisition parameters such as aperture, shutter speed, flash power, gain, color balance, white point, or focus. In addition, a default set of image attributes, particularly related to potential faces in the image, are loaded at 1002. Such attributes can be the overall color balance, exposure, contrast, orientation etc. Alternatively, at 1003, a collection of preview images may be analyzed to determine the potential existence of faces in the picture at 1006. A region wherein potentially the eyes or mouth will be when the full resolution is captured may also be predicted at 1008. This alternative technique may include moving on to block 1010 and/or 1002.
An image is then digitally captured onto the sensor at 1010. Such action may be continuously updated, and may or may not include saving such captured image into permanent storage.
An image-detection process, preferably a face detection process, as known to one familiar in the art of image classification and face detection in particular, is applied to the captured image to seek eyes, mouths or faces or other features in the image at 1020. Such face detection techniques, include, but are not limited to: knowledge-based; feature-invariant; template-matching; appearance-based; color or motion cues; adaboost-based face detector, Viola-Jones, etc.
If no images are found, the process terminates at 1032. Alternatively, or in addition to the automatic detection of 1030, the user can manually select, 1034 detected mouths or faces, using some interactive user interface mechanism, by utilizing, for example, a camera display. Alternatively, the process can be implemented without a visual user interface by changing the sensitivity or threshold of the detection process. Alternatively, this data may be available form a pre-capture process 1003.
When mouths or faces are detected, 1040, they are marked, and labeled. Detecting defined in 1040 may be more than a binary process of selecting whether a mouth or a face is detected or not, it may also be designed as part of a process where each of the mouths or faces is given a weight based on size of the mouths or faces, location within the frame, other parameters described herein, which define the importance of the mouth or face in relation to other mouths or faces detected.
Alternatively, or in addition, the user can manually deselect regions 1044 that were wrongly false detected as mouths or faces. Such selection can be due to the fact that a mouth or a face was false detected or when the photographer may wish to concentrate on one of the mouths or faces as the main subject matter and not on other mouths or faces. Alternatively, 1046 the user may re-select, or emphasize one or more mouths or faces to indicate that these mouths or faces have a higher importance in the calculation relative to other mouths or faces. This process as defined in 1046 further defines the preferred identification process to be a continuous value one as opposed to a binary one. The process can be done utilizing a visual user interface or by adjusting the sensitivity of the detection process.
After the mouths or faces or other features are correctly isolated at 1040 their attributes are compared at 1050 to default values that were predefined in 1002. Such comparison will determine a potential transformation between the two images, in order to reach the same values. The transformation is then translated to the camera capture parameters 1070 and the image is acquired 1090.
A practical example is that if the captured face is too dark, the acquisition parameters may change to allow a longer exposure, or open the aperture. Note that the image attributes are not necessarily only related to the face regions but can also be in relations to the overall exposure. As an exemplification, if the overall exposure is correct but the faces are underexposed, the camera may shift into a fill-flash mode.
At 1060, capture is delayed until detected image attributes match default image attributes. An example in accordance with a preferred embodiment is to delay capture until mouths that are frowning and causing the delay are no longer frowning. At 1070, manual override instructions may be entered to take the picture anyway, or to keep a picture or to continue processing of a picture, even though frowning is detected within the picture. The picture is taken at 1090, or in accordance with another embodiment, the picture is stored in full resolution.
Referring to FIG. 6 b, a process is described for using mouth, face or other feature detection to improve output or rendering parameters, as aforementioned in FIG. 5, block 1103. In this scenario, a rendering device such as a printer or a display, hereinafter referred to as “the device” is activated at 2100. Such activation can be performed for example within a printer, or alternatively within a device connected to the printer such as a PC or a camera. The device then goes through a normal pre-rendering stage to determine at 2104, the correct rendering parameters such as tone reproduction, color transformation profiles, gain, color balance, white point and resolution. In addition, a default set of image attributes, particularly related to potential mouths or faces in the image, are loaded at 2102. Such attributes can be the overall color balance, exposure, contrast, or orientation, or combinations thereof.
An image is then digitally downloaded onto the device 2110. An image-detection process, preferably a mouth or a face detection process, is applied to the downloaded image to seek mouths or faces in the image at 2120. If no images are found, the process terminates at 2132 and the device resumes its normal rendering process. Alternatively, or in addition to the automatic detection of 2130, the user can manually select 2134 detected mouths or faces or other features, using some interactive user interface mechanism, by utilizing, for example, a display on the device. Alternatively, the process can be implemented without a visual user interface by changing the sensitivity or threshold of the detection process.
When mouths or faces are detected at 2130, they are marked at 2140, and labeled. Detecting in 2130 may be more than a binary process of selecting whether a mouth or a face is detected or not. It may also be designed as part of a process where each of the mouths or faces is given a weight based on size of the faces, location within the frame, other parameters described herein, etc., which define the importance of the mouth or face in relation to other mouths or faces detected.
Alternatively, or in addition, the user can manually deselect regions at 2144 that were wrongly false detected as mouths or faces. Such selection can be due to the fact that a mouth or face was false detected or when the photographer may wish to concentrate on a mouth or a faces as the main subject matter and not on other mouths or faces. Alternatively, 2146, the user may re-select, or emphasize one or more mouths or faces to indicate that these mouths or faces have a higher importance in the calculation relative to other mouths or faces. This process as defined in 1146, further defines the preferred identification process to be a continuous value one as opposed to a binary one. The process can be done utilizing a visual user interface or by adjusting the sensitivity of the detection process.
After the mouths or faces or other scene or image features are correctly isolated at 2140, their attributes are compared at 2150 to default values that were predefined in 2102. At least one preferred attribute that the process is looking for is frowning mouths. Such comparison will determine a potential transformation between the two images, in order to reach the same values. The image may be disqualified at 2160 if one or more mouths are determined to be frowning. The disqualifying may be overridden manually at 2170 or open mouth pixels may be substituted from a different image. The transformation may be translated to the device rendering parameters, and the image at 2190 may be rendered. The process may include a plurality of images. In this case at 2180, the process repeats itself for each image prior to performing the rendering process. A practical example is the creation of a thumbnail or contact sheet which is a collection of low resolution images, on a single display instance.
A practical example is that if the mouth or face were too darkly captured, the rendering parameters may change the tone reproduction curve to lighten the mouth or face. Note that the image attributes are not necessarily only related to the mouth or face regions, but can also be in relation to an overall tone reproduction.
Referring to FIGS. 7 a-7 d, which describe automatic rotation of an image based on the location and orientation of mouths, eyes, faces, other face features, or other non-facial features, as highlighted in FIG. 5 at Block 1130. An image of two faces is provided in FIG. 7 a. Note that the faces may not be identically oriented, and that the faces may be occluding. In this case, both eyes are showing on each face, but only one eye might be showing. Also, both mouths are showing, but one or both could be missing in other scenes.
The software in the mouth or face detection stage, including the functionality of FIG. 5, blocks 1108 and 1118, will mark the two faces or the two mouths or four eyes of the mother and son, e.g., the faces may be marked as estimations of ellipses 2100 and 2200, respectively. Using known mathematical means, such as the covariance matrices of the ellipses, the software will determine the main axes of the two faces 2120 and 2220, respectively as well as the secondary axis 2140 and 2240. Even at this stage, by merely comparing the sizes of the axes, the software may assume that the image is oriented 90 degrees, in the case that the camera is in landscape mode, which is horizontal, or in portrait mode which is vertical or +90 degrees, aka clockwise, or −90 degrees aka counter clockwise. Alternatively, the application may also be utilized for any arbitrary rotation value. However, this information may not suffice to decide whether the image is rotated clockwise or counter-clockwise.
FIG. 7 c describes the step of extracting the pertinent features of a face, which are usually highly detectable. Such objects may include the eyes, 2140, 2160 and 2240, 2260, and the lips, 2180 and 2280, or the nose, eye brows, eye lids, features of the eyes, hair, forehead, chin, ears, etc. The combination of the two eyes and the center of the lips creates a triangle 2300 which can be detected not only to determine the orientation of the face but also the rotation of the face relative to a facial shot. Note that there are other highly detectable portions of the image which can be labeled and used for orientation detection, such as the nostrils, the eyebrows, the hair line, nose-bridge and the neck as the physical extension of the face, etc. In this figure, the eyes and lips are provided as an example of such facial features Based on the location of the eyes, if found, and the mouth, the image might ought to be rotated in a counter clockwise direction.
Note that it may not be enough to just locate the different facial features, but such features may be compared to each other. For example, the color of the eyes may be compared to ensure that the pair of eyes originated from the same person. Alternatively, the features of the face may be compared with preview images. Such usage may prevent a case where a double upper eyelid may be mistaken to a semi closed eye. Another example is that in FIGS. 7 c and 7 d, if the software combined the mouth of 2180 with the eyes of 2260, 2240, the orientation would have been determined as clockwise. In this case, the software detects the correct orientation by comparing the relative size of the mouth and the eyes. The above method describes exemplary and illustrative techniques for determining the orientation of the image based on the relative location of the different facial objects. For example, it may be desired that the two eyes should be horizontally situated, the nose line perpendicular to the eyes, the mouth under the nose etc. Alternatively, orientation may be determined based on the geometry of the facial components themselves. For example, it may be desired that the eyes are elongated horizontally, which means that when fitting an ellipse on the eye, such as described in blocs 2140 and 2160, it may be desired that the main axis should be horizontal. Similar with the lips which when fitted to an ellipse the main axis should be horizontal. Alternatively, the region around the face may also be considered. In particular, the neck and shoulders which are the only contiguous skin tone connected to the head can be an indication of the orientation and detection of the face.
The process for determining the orientation of images can be implemented in a preferred embodiment as part of a digital display device. Alternatively, this process can be implemented as part of a digital printing device, or within a digital acquisition device.
The process can also be implemented as part of a display of multiple images on the same page or screen such as in the display of a contact-sheet or a thumbnail view of images. In this case, the user may approve or reject the proposed orientation of the images individually or by selecting multiple images at once. In the case of a sequence of images, the orientation of images may be determined based on the information as approved by the user regarding previous images.
Alternatively, as described by the flow chart of FIG. 8 a, a similar method may be utilized in the pre-acquisition stage, to determine if digital simulation or re-compositioning of an image with non-frowning mouths may be advantageous or not, e.g., when a mouth is determined to be frowning. U.S. Pat. No. 6,151,073 to Steinberg et al. is hereby incorporated by reference. In block 1108 of FIG. 5, the camera searched for the existence of mouths, eyes or faces in the image. At 1460, it is determined whether one or more mouths were found in the image. If not, then exit at 1462. If so, then the mouths are marked at 1464. The mouth regions are analyzed at 1470. If the mouths are determined to be sufficiently configured as non-frowning at 1474, then the image is left as is at 1478. However, if the mouths are determined to be unsatisfactorily frowning, or the lips are turned downward at the edges beyond a threshold amount, or not turned upwards sufficiently, then the process can proceed to correction at 1480, 1490 and/or 1494. At 1480, a sub-routine for digitally simulating non-frowning mouths is provided. A mask or masks define selected regions, i.e., in this example, eye regions. The exposure may be increased at 1484 or that may be skipped. Shape and/or color processing is performed at 1486 to the selected mouth regions. For example, where frowning lips exist in the original image, non-frowning lips are provided to be substituted over the frowning lips. Tone reproduction is provided at 1488.
At 1490, single or multiple results may be provided to a user. The user may select a preferred result at 1492, and the correction is applied at 1498. Alternatively, the image may be displayed at 1494 to the user with a parameter to be modified such as lips configuration. The user then adjusts the extent of the modification at 1496, and the image is corrected at 1498.
FIG. 8 b provides another workflow wherein picture taking mode is initiated at 1104 as in FIG. 5. The image is analyzed at 4820. A determination of whether mouths were found in the image is made at 1106. If not, then exit at 1110. If so, then the mouths are marked at 1108. The mouth regions are analyzed at 4840, and if the mouths are open 4960, then the picture is either taken, stored (e.g., if the picture was previously taken) or taken and stored at 4880. If the mouths are determined to be frowning at 4860, e.g., because the person appears to be unhappy, then the image may be discarded or image capture delayed at 4980, or alternatively the picture may be taken at 4900. In this latter embodiment, a non-frowning mouth region is substituted for pixels of the frowning mouth at 4920, and the combination picture is stored at 4940.
FIG. 9 illustrates a technique involving motion of lips. A focusing mechanism is activated at 1170. The camera seeks the mouth and/or lips at 1750. If a mouth is not detected at 1760, then spatial based auto focusing techniques may be performed at 1762. If a mouth is detected, then regions are marked at 1770. The regions are displayed at 1772. The user may take the picture now at 1790. However, the user may move to focus tracking mode at 1780. While the lips are moving, e.g., in the process of frowning or ending a frown 1782, the lip movement is tracked at 1784. A delay or scene disqualification is imposed while the lips are moving during the frowning process at 1786. When the disqualifying period ends, the user may take the picture, or the camera may be programmed to automatically take the shot at 1790.
What follows is a cite list of references which are, in addition to that which is described as background, the invention summary, the abstract, the brief description of the drawings and the drawings, and other references cited above, hereby incorporated by reference into the detailed description of the preferred embodiments as disclosing alternative embodiments:
U.S. Pat. Nos. 6,965,684, 6,301,440, RE33682, RE31370, 4,047,187, 4,317,991, 4,367,027, 4,638,364, 5,291,234, 5,488,429, 5,638,136, 5,710,833, 5,724,456, 5,781,650, 5,812,193, 5,818,975, 5,835,616, 5,870,138, 5,978,519, 5,991,456, 6,097,470, 6,101,271, 6,128,397, 6,148,092, 6,151,073, 6,188,777, 6,192,149, 6,249,315, 6,263,113, 6,268,939, 6,282,317, 6,301,370, 6,332,033, 6,393,148, 6,404,900, 6,407,777, 6,421,468, 6,438,264, 6,456,732, 6,459,436, 6,473,199, 6,501,857, 6,504,942, 6,504,951, 6,516,154, and 6,526,161;
United States published patent applications no. 2003/0071908, 2003/0052991, 2003/0025812, 2002/0172419, 2002/0114535, 2002/0105662, and 2001/0031142;
U.S. provisional application No. 60/776,338, entitled Human Eye Detector;
Japanese patent application no. JP5260360A2;
British patent application no. GB0031423.7;
Yang et al., IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 24, no. 1, pp 34-58 (January 2002); and
Baluja & Rowley, “Neural Network-Based Face Detection,” IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 20, No. 1, pages 23-28, January 1998.
While an exemplary drawings and specific embodiments of the present invention have been described and illustrated, it is to be understood that that the scope of the present invention is not to be limited to the particular embodiments discussed. Thus, the embodiments shall be regarded as illustrative rather than restrictive, and it should be understood that variations may be made in those embodiments by workers skilled in the arts without departing from the scope of the present invention as set forth in the claims that follow and their structural and functional equivalents.
In addition, in methods that may be performed according to the claims below and/or preferred embodiments herein, the operations have been described in selected typographical sequences. However, the sequences have been selected and so ordered for typographical convenience and are not intended to imply any particular order for performing the operations, unless a particular ordering is expressly provided or understood by those skilled in the art as being necessary.

Claims (27)

1. A method of selectively disqualifying a scene as a candidate for permanent capture, storage or processing, or combinations thereof, the method comprising:
detecting and tracking a face, partial face or eye within a scene in a preacquisition stage, in a video stream or within a collection of preview images, or combinations thereof;
performing a shape analysis of the eye;
determining that the eye is currently in a blinking process;
rejecting the scene as a candidate for digital image capture due to the blinking; and
automatically acquiring a digital image after delaying for a period of time, wherein the delaying of said acquiring the digital image corresponds to an estimated time for said blinking process to end based on said shape analysis of said eye.
2. The method of claim 1, wherein the detecting and tracking comprises acquiring a collection of preview images.
3. The method of claim 1, wherein the temporal analysis comprises determining a speed at which the eye lid is closing or opening the eye.
4. The method of claim 1, wherein the temporal analysis comprises determining a speed of the eye lid for determining when the blinking process will end.
5. The method of claim 1, wherein the temporal analysis comprises a speed and a direction of the movement of the eye lid.
6. The method of claim 1, further comprising determining an extent to which the eye ball of the eye is showing.
7. The method of claim 6, further comprising determining whether the eye ball is currently being covered or uncovered.
8. The method of claim 1, wherein the determining temporal analysis of movement comprises determining a direction of movement of the eye lid.
9. The method of claim 1, wherein the performing temporal analysis comprises determining a degree of blurriness of the eye lid due to movement.
10. One or more non-transitory processor-readable media having code embedded therein for programming a processor to perform a method of selectively disqualifying a scene as a candidate for permanent capture, storage or processing, or combinations thereof, the method comprising:
detecting and tracking a face, partial face or eye within a scene in a preacquisition stage, in a video stream or within a collection of preview images, or combinations thereof;
performing a shape analysis of the eye;
determining that the eye is currently in a blinking process;
rejecting the scene as a candidate for digital image capture due to the blinking; and
automatically acquiring a digital image after delaying for a period of time, wherein the delaying of said acquiring the digital image corresponds to an estimated time for said blinking process to end based on said shape analysis of said eye.
11. The one or more non-transitory processor-readable media of claim 10, wherein the detecting and tracking comprises acquiring a collection of preview images.
12. The one or more non-transitory processor-readable media of claim 10, wherein the temporal analysis comprises determining a speed at which the eye lid is closing or opening the eye.
13. The one or more non-transitory processor-readable media of claim 10, wherein the temporal analysis comprises determining a speed of the eye lid for determining when the blinking process will end.
14. The one or more non-transitory processor-readable media of claim 10, wherein the temporal analysis comprises a speed and a direction of the movement of the eye lid.
15. The one or more non-transitory processor-readable media of claim 10, the method further comprising determining an extent to which the eye ball of the eye is showing.
16. The one or more non-transitory processor-readable media of claim 15, the method further comprising determining whether the eye ball is currently being covered or uncovered.
17. The one or more non-transitory processor-readable media of claim 10, wherein the determining temporal analysis of movement comprises determining a direction of movement of the eye lid.
18. The one or more non-transitory processor-readable media of claim 10, wherein the performing temporal analysis comprises determining a degree of blurriness of the eye lid due to movement.
19. A digital image acquisition device, comprising:
a lens and image sensor for acquiring digital images;
a processor; and
one or more processor-readable media having code embedded therein for programming the processor to perform a method of selectively disqualifying a scene as a candidate for permanent capture, storage or processing, or combinations thereof, wherein the method comprises:
detecting and tracking a face, partial face or eye within a scene in a preacquisition stage, in a video stream or within a collection of preview images, or combinations thereof;
performing a shape analysis of the eye;
determining that the eye is currently in a blinking process;
rejecting the scene as a candidate for digital image capture due to the blinking; and
automatically acquiring a digital image after delaying for a period of time, wherein the delaying of said acquiring the digital image corresponds to an estimated time for said blinking process to end based on said shape analysis of said eye.
20. The device of claim 19, wherein the detecting and tracking comprises acquiring a collection of preview images.
21. The device of claim 19, wherein the temporal analysis comprises determining a speed at which the eye lid is closing or opening the eye.
22. The device of claim 19, wherein the temporal analysis comprises determining a speed of the eye lid for determining when the blinking process will end.
23. The device of claim 19, wherein the temporal analysis comprises a speed and a direction of the movement of the eye lid.
24. The device of claim 19, the method further comprising determining an extent to which the eye ball of the eye is showing.
25. The device of claim 24, the method further comprising determining whether the eye ball is currently being covered or uncovered.
26. The device of claim 19, wherein the determining temporal analysis of movement comprises determining a direction of movement of the eye lid.
27. The device of claim 19, wherein the performing temporal analysis comprises determining a degree of blurriness of the eye lid due to movement.
US13/191,239 2006-02-24 2011-07-26 Digital image acquisition control and correction method and apparatus Active US8265348B2 (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
US13/191,239 US8265348B2 (en) 2006-02-24 2011-07-26 Digital image acquisition control and correction method and apparatus

Applications Claiming Priority (4)

Application Number Priority Date Filing Date Title
US77633806P 2006-02-24 2006-02-24
US11/460,225 US7804983B2 (en) 2006-02-24 2006-07-26 Digital image acquisition control and correction method and apparatus
US12/851,333 US8005268B2 (en) 2006-02-24 2010-08-05 Digital image acquisition control and correction method and apparatus
US13/191,239 US8265348B2 (en) 2006-02-24 2011-07-26 Digital image acquisition control and correction method and apparatus

Related Parent Applications (1)

Application Number Title Priority Date Filing Date
US12/851,333 Continuation US8005268B2 (en) 2006-02-24 2010-08-05 Digital image acquisition control and correction method and apparatus

Publications (2)

Publication Number Publication Date
US20110279700A1 US20110279700A1 (en) 2011-11-17
US8265348B2 true US8265348B2 (en) 2012-09-11

Family

ID=40616746

Family Applications (3)

Application Number Title Priority Date Filing Date
US11/460,225 Active 2029-03-31 US7804983B2 (en) 2006-02-24 2006-07-26 Digital image acquisition control and correction method and apparatus
US12/851,333 Active US8005268B2 (en) 2006-02-24 2010-08-05 Digital image acquisition control and correction method and apparatus
US13/191,239 Active US8265348B2 (en) 2006-02-24 2011-07-26 Digital image acquisition control and correction method and apparatus

Family Applications Before (2)

Application Number Title Priority Date Filing Date
US11/460,225 Active 2029-03-31 US7804983B2 (en) 2006-02-24 2006-07-26 Digital image acquisition control and correction method and apparatus
US12/851,333 Active US8005268B2 (en) 2006-02-24 2010-08-05 Digital image acquisition control and correction method and apparatus

Country Status (2)

Country Link
US (3) US7804983B2 (en)
CN (2) CN101427266B (en)

Cited By (14)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20120269428A1 (en) * 2011-04-25 2012-10-25 Daniel Bloom Mouth Corner Candidates
US8633999B2 (en) 2009-05-29 2014-01-21 DigitalOptics Corporation Europe Limited Methods and apparatuses for foreground, top-of-the-head separation from background
US8971628B2 (en) 2010-07-26 2015-03-03 Fotonation Limited Face detection using division-generated haar-like features for illumination invariance
WO2015162605A2 (en) 2014-04-22 2015-10-29 Snapaid Ltd System and method for controlling a camera based on processing an image captured by other camera
US9754163B2 (en) 2015-06-22 2017-09-05 Photomyne Ltd. System and method for detecting objects in an image
US9769367B2 (en) 2015-08-07 2017-09-19 Google Inc. Speech and computer vision-based control
US9838641B1 (en) 2015-12-30 2017-12-05 Google Llc Low power framework for processing, compressing, and transmitting images at a mobile image capture device
US9836819B1 (en) 2015-12-30 2017-12-05 Google Llc Systems and methods for selective retention and editing of images captured by mobile image capture device
US9836484B1 (en) 2015-12-30 2017-12-05 Google Llc Systems and methods that leverage deep learning to selectively store images at a mobile image capture device
US10101636B2 (en) 2012-12-31 2018-10-16 Digitaloptics Corporation Auto-focus camera module with MEMS capacitance estimator
US10225511B1 (en) 2015-12-30 2019-03-05 Google Llc Low power framework for controlling image sensor mode in a mobile image capture device
US10419655B2 (en) 2015-04-27 2019-09-17 Snap-Aid Patents Ltd. Estimating and using relative head pose and camera field-of-view
US10732809B2 (en) 2015-12-30 2020-08-04 Google Llc Systems and methods for selective retention and editing of images captured by mobile image capture device
US11727426B2 (en) 2013-05-21 2023-08-15 Fotonation Limited Anonymizing facial expression data with a smart-cam

Families Citing this family (103)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US7574016B2 (en) 2003-06-26 2009-08-11 Fotonation Vision Limited Digital image processing using face detection information
US8494286B2 (en) * 2008-02-05 2013-07-23 DigitalOptics Corporation Europe Limited Face detection in mid-shot digital images
US7440593B1 (en) 2003-06-26 2008-10-21 Fotonation Vision Limited Method of improving orientation and color balance of digital images using face detection information
US9129381B2 (en) 2003-06-26 2015-09-08 Fotonation Limited Modification of post-viewing parameters for digital images using image region or feature information
US7269292B2 (en) 2003-06-26 2007-09-11 Fotonation Vision Limited Digital image adjustable compression and resolution using face detection information
US8989453B2 (en) 2003-06-26 2015-03-24 Fotonation Limited Digital image processing using face detection information
US7620218B2 (en) 2006-08-11 2009-11-17 Fotonation Ireland Limited Real-time face tracking with reference images
US8896725B2 (en) * 2007-06-21 2014-11-25 Fotonation Limited Image capture device with contemporaneous reference image capture mechanism
US8682097B2 (en) 2006-02-14 2014-03-25 DigitalOptics Corporation Europe Limited Digital image enhancement with reference images
US7471846B2 (en) 2003-06-26 2008-12-30 Fotonation Vision Limited Perfecting the effect of flash within an image acquisition devices using face detection
US8330831B2 (en) 2003-08-05 2012-12-11 DigitalOptics Corporation Europe Limited Method of gathering visual meta data using a reference image
US8948468B2 (en) 2003-06-26 2015-02-03 Fotonation Limited Modification of viewing parameters for digital images using face detection information
US8155397B2 (en) 2007-09-26 2012-04-10 DigitalOptics Corporation Europe Limited Face tracking in a camera processor
US7565030B2 (en) 2003-06-26 2009-07-21 Fotonation Vision Limited Detecting orientation of digital images using face detection information
US8498452B2 (en) 2003-06-26 2013-07-30 DigitalOptics Corporation Europe Limited Digital image processing using face detection information
US7792335B2 (en) 2006-02-24 2010-09-07 Fotonation Vision Limited Method and apparatus for selective disqualification of digital images
US9692964B2 (en) 2003-06-26 2017-06-27 Fotonation Limited Modification of post-viewing parameters for digital images using image region or feature information
US7685341B2 (en) * 2005-05-06 2010-03-23 Fotonation Vision Limited Remote control apparatus for consumer electronic appliances
US7792970B2 (en) 2005-06-17 2010-09-07 Fotonation Vision Limited Method for establishing a paired connection between media devices
US8593542B2 (en) 2005-12-27 2013-11-26 DigitalOptics Corporation Europe Limited Foreground/background separation using reference images
US7844076B2 (en) 2003-06-26 2010-11-30 Fotonation Vision Limited Digital image processing using face detection and skin tone information
JP2005346806A (en) * 2004-06-02 2005-12-15 Funai Electric Co Ltd Dvd recorder and recording and reproducing apparatus
US8320641B2 (en) 2004-10-28 2012-11-27 DigitalOptics Corporation Europe Limited Method and apparatus for red-eye detection using preview or other reference images
US8488023B2 (en) * 2009-05-20 2013-07-16 DigitalOptics Corporation Europe Limited Identifying facial expressions in acquired digital images
US8995715B2 (en) 2010-10-26 2015-03-31 Fotonation Limited Face or other object detection including template matching
US7315631B1 (en) 2006-08-11 2008-01-01 Fotonation Vision Limited Real-time face tracking in a digital image acquisition device
US20110102553A1 (en) * 2007-02-28 2011-05-05 Tessera Technologies Ireland Limited Enhanced real-time face models from stereo imaging
US7715597B2 (en) 2004-12-29 2010-05-11 Fotonation Ireland Limited Method and component for image recognition
US8503800B2 (en) 2007-03-05 2013-08-06 DigitalOptics Corporation Europe Limited Illumination detection using classifier chains
US7694048B2 (en) * 2005-05-06 2010-04-06 Fotonation Vision Limited Remote control apparatus for printer appliances
US7804983B2 (en) * 2006-02-24 2010-09-28 Fotonation Vision Limited Digital image acquisition control and correction method and apparatus
WO2008023280A2 (en) 2006-06-12 2008-02-28 Fotonation Vision Limited Advances in extending the aam techniques from grayscale to color images
US7916897B2 (en) 2006-08-11 2011-03-29 Tessera Technologies Ireland Limited Face tracking for controlling imaging parameters
US7403643B2 (en) 2006-08-11 2008-07-22 Fotonation Vision Limited Real-time face tracking in a digital image acquisition device
US8055067B2 (en) 2007-01-18 2011-11-08 DigitalOptics Corporation Europe Limited Color segmentation
JP5049356B2 (en) 2007-02-28 2012-10-17 デジタルオプティックス・コーポレイション・ヨーロッパ・リミテッド Separation of directional lighting variability in statistical face modeling based on texture space decomposition
FR2913510B1 (en) * 2007-03-07 2009-07-03 Eastman Kodak Co METHOD FOR AUTOMATICALLY DETERMINING A PROBABILITY OF IMAGE ENTRY WITH A TERMINAL BASED ON CONTEXTUAL DATA
US7916971B2 (en) 2007-05-24 2011-03-29 Tessera Technologies Ireland Limited Image processing method and apparatus
JP4853425B2 (en) * 2007-08-14 2012-01-11 ソニー株式会社 Imaging apparatus, imaging method, and program
US8031970B2 (en) * 2007-08-27 2011-10-04 Arcsoft, Inc. Method of restoring closed-eye portrait photo
US8750578B2 (en) 2008-01-29 2014-06-10 DigitalOptics Corporation Europe Limited Detecting facial expressions in digital images
US8170298B2 (en) * 2008-05-16 2012-05-01 Arcsoft, Inc. Method for detecting facial expression and repairing smile face of portrait photo
WO2010012448A2 (en) 2008-07-30 2010-02-04 Fotonation Ireland Limited Automatic face and skin beautification using face detection
JP5361547B2 (en) * 2008-08-07 2013-12-04 キヤノン株式会社 Imaging apparatus, imaging method, and program
US8686953B2 (en) * 2008-09-12 2014-04-01 Qualcomm Incorporated Orienting a displayed element relative to a user
CN102203850A (en) * 2008-09-12 2011-09-28 格斯图尔泰克公司 Orienting displayed elements relative to a user
US8379917B2 (en) 2009-10-02 2013-02-19 DigitalOptics Corporation Europe Limited Face recognition performance using additional image features
US10080006B2 (en) 2009-12-11 2018-09-18 Fotonation Limited Stereoscopic (3D) panorama creation on handheld device
US8872887B2 (en) 2010-03-05 2014-10-28 Fotonation Limited Object detection and rendering for wide field of view (WFOV) image acquisition systems
CN101800816B (en) * 2010-04-08 2012-10-17 华为终端有限公司 Method for horizontal and vertical switching of touch screen of mobile terminal and mobile terminal
US9053681B2 (en) 2010-07-07 2015-06-09 Fotonation Limited Real-time video frame pre-processing hardware
CN103098078B (en) * 2010-09-13 2017-08-15 惠普发展公司,有限责任合伙企业 Smile's detecting system and method
US8970770B2 (en) 2010-09-28 2015-03-03 Fotonation Limited Continuous autofocus based on face detection and tracking
US8648959B2 (en) 2010-11-11 2014-02-11 DigitalOptics Corporation Europe Limited Rapid auto-focus using classifier chains, MEMS and/or multiple object focusing
US8659697B2 (en) 2010-11-11 2014-02-25 DigitalOptics Corporation Europe Limited Rapid auto-focus using classifier chains, MEMS and/or multiple object focusing
US8308379B2 (en) 2010-12-01 2012-11-13 Digitaloptics Corporation Three-pole tilt control system for camera module
US8836777B2 (en) 2011-02-25 2014-09-16 DigitalOptics Corporation Europe Limited Automatic detection of vertical gaze using an embedded imaging device
US8723959B2 (en) 2011-03-31 2014-05-13 DigitalOptics Corporation Europe Limited Face and other object tracking in off-center peripheral regions for nonlinear lens geometries
US8896703B2 (en) 2011-03-31 2014-11-25 Fotonation Limited Superresolution enhancment of peripheral regions in nonlinear lens geometries
US8982180B2 (en) 2011-03-31 2015-03-17 Fotonation Limited Face and other object detection and tracking in off-center peripheral regions for nonlinear lens geometries
US8947501B2 (en) 2011-03-31 2015-02-03 Fotonation Limited Scene enhancements in off-center peripheral regions for nonlinear lens geometries
US8564684B2 (en) * 2011-08-17 2013-10-22 Digimarc Corporation Emotional illumination, and related arrangements
JP5859771B2 (en) * 2011-08-22 2016-02-16 ソニー株式会社 Information processing apparatus, information processing system information processing method, and program
US9111144B2 (en) * 2011-09-15 2015-08-18 Identigene, L.L.C. Eye color paternity test
US9354486B2 (en) 2012-06-07 2016-05-31 DigitalOptics Corporation MEMS MEMS fast focus camera module
US9007520B2 (en) 2012-08-10 2015-04-14 Nanchang O-Film Optoelectronics Technology Ltd Camera module with EMI shield
US9001268B2 (en) 2012-08-10 2015-04-07 Nan Chang O-Film Optoelectronics Technology Ltd Auto-focus camera module with flexible printed circuit extension
US9242602B2 (en) 2012-08-27 2016-01-26 Fotonation Limited Rearview imaging systems for vehicle
WO2014064690A1 (en) 2012-10-23 2014-05-01 Sivan Ishay Real time assessment of picture quality
KR102003371B1 (en) 2012-11-16 2019-10-01 삼성전자주식회사 Apparatas and method for adjusting of screen brightness in an electronic device
KR102076773B1 (en) * 2013-01-04 2020-02-12 삼성전자주식회사 Method for obtaining video data and an electronic device thereof
US9055210B2 (en) 2013-06-19 2015-06-09 Blackberry Limited Device for detecting a camera obstruction
EP2816797A1 (en) * 2013-06-19 2014-12-24 BlackBerry Limited Device for detecting a camera obstruction
CN103369248A (en) * 2013-07-20 2013-10-23 厦门美图移动科技有限公司 Method for photographing allowing closed eyes to be opened
KR102127351B1 (en) * 2013-07-23 2020-06-26 삼성전자주식회사 User terminal device and the control method thereof
JP5971216B2 (en) * 2013-09-20 2016-08-17 カシオ計算機株式会社 Image processing apparatus, image processing method, and program
WO2015044947A1 (en) * 2013-09-30 2015-04-02 Yanai Danielle Image and video processing and optimization
US20160292535A1 (en) * 2013-12-24 2016-10-06 Sony Corporation An emotion based power efficient self-portrait mechanism
US9549118B2 (en) * 2014-03-10 2017-01-17 Qualcomm Incorporated Blink and averted gaze avoidance in photographic images
US9316820B1 (en) 2014-03-16 2016-04-19 Hyperion Development, LLC Optical assembly for a wide field of view point action camera with low astigmatism
US9091843B1 (en) 2014-03-16 2015-07-28 Hyperion Development, LLC Optical assembly for a wide field of view point action camera with low track length to focal length ratio
US9995910B1 (en) 2014-03-16 2018-06-12 Navitar Industries, Llc Optical assembly for a compact wide field of view digital camera with high MTF
US10139595B1 (en) 2014-03-16 2018-11-27 Navitar Industries, Llc Optical assembly for a compact wide field of view digital camera with low first lens diameter to image diagonal ratio
US9494772B1 (en) 2014-03-16 2016-11-15 Hyperion Development, LLC Optical assembly for a wide field of view point action camera with low field curvature
US10545314B1 (en) 2014-03-16 2020-01-28 Navitar Industries, Llc Optical assembly for a compact wide field of view digital camera with low lateral chromatic aberration
US10386604B1 (en) 2014-03-16 2019-08-20 Navitar Industries, Llc Compact wide field of view digital camera with stray light impact suppression
US9316808B1 (en) 2014-03-16 2016-04-19 Hyperion Development, LLC Optical assembly for a wide field of view point action camera with a low sag aspheric lens element
US9726859B1 (en) 2014-03-16 2017-08-08 Navitar Industries, Llc Optical assembly for a wide field of view camera with low TV distortion
CN104468578B (en) * 2014-12-10 2017-12-26 怀效宁 The priority traffic system and the means of communication of a kind of wireless telecommunications
JP2016076869A (en) * 2014-10-08 2016-05-12 オリンパス株式会社 Imaging apparatus, imaging method and program
CN104394461A (en) * 2014-11-12 2015-03-04 无锡科思电子科技有限公司 Television self-adaption shutdown control method
CN104599367A (en) * 2014-12-31 2015-05-06 苏州福丰科技有限公司 Multi-user parallel access control recognition method based on three-dimensional face image recognition
US10198819B2 (en) * 2015-11-30 2019-02-05 Snap Inc. Image segmentation and modification of a video stream
CN106210526A (en) * 2016-07-29 2016-12-07 维沃移动通信有限公司 A kind of image pickup method and mobile terminal
US10670842B2 (en) 2017-01-26 2020-06-02 Navitar, Inc. High Etendue zoom lens having five lens groups
CN108319953B (en) * 2017-07-27 2019-07-16 腾讯科技(深圳)有限公司 Occlusion detection method and device, electronic equipment and the storage medium of target object
CN108492266B (en) * 2018-03-18 2020-10-09 Oppo广东移动通信有限公司 Image processing method, image processing device, storage medium and electronic equipment
US11113507B2 (en) * 2018-05-22 2021-09-07 Samsung Electronics Co., Ltd. System and method for fast object detection
JP6973298B2 (en) * 2018-05-31 2021-11-24 トヨタ自動車株式会社 Object monitoring device
CN108989677A (en) * 2018-07-27 2018-12-11 上海与德科技有限公司 A kind of automatic photographing method, device, server and storage medium
CN110008802B (en) 2018-12-04 2023-08-29 创新先进技术有限公司 Method and device for selecting target face from multiple faces and comparing face recognition
CN111126289A (en) * 2019-12-25 2020-05-08 航天信息股份有限公司 Method and system for acquiring portrait based on thread control
CN114554113B (en) * 2022-04-24 2022-08-16 浙江华眼视觉科技有限公司 Express item code recognition machine express item person drawing method and device

Citations (164)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US4047187A (en) 1974-04-01 1977-09-06 Canon Kabushiki Kaisha System for exposure measurement and/or focus detection by means of image senser
US4299464A (en) * 1980-08-04 1981-11-10 Eastman Kodak Company Method and apparatus for reducing the incidence of eye closures during photographing of a human subject
US4317991A (en) 1980-03-12 1982-03-02 Honeywell Inc. Digital auto focus system utilizing a photodetector array
US4367027A (en) 1980-03-12 1983-01-04 Honeywell Inc. Active auto focus system improvement
US4638364A (en) 1984-10-30 1987-01-20 Sanyo Electric Co., Ltd. Auto focus circuit for video camera
US5018017A (en) 1987-12-25 1991-05-21 Kabushiki Kaisha Toshiba Electronic still camera and image recording method thereof
US5063603A (en) 1989-11-06 1991-11-05 David Sarnoff Research Center, Inc. Dynamic method for recognizing objects and image processing system therefor
US5164992A (en) 1990-11-01 1992-11-17 Massachusetts Institute Of Technology Face recognition system
US5164831A (en) 1990-03-15 1992-11-17 Eastman Kodak Company Electronic still camera providing multi-format storage of full and reduced resolution images
US5227837A (en) 1989-05-12 1993-07-13 Fuji Photo Film Co., Ltd. Photograph printing method
US5280530A (en) 1990-09-07 1994-01-18 U.S. Philips Corporation Method and apparatus for tracking a moving object
US5291234A (en) 1987-02-04 1994-03-01 Asahi Kogaku Kogyo Kabushiki Kaisha Auto optical focus detecting device and eye direction detecting optical system
US5311240A (en) 1992-11-03 1994-05-10 Eastman Kodak Company Technique suited for use in multi-zone autofocusing cameras for improving image quality for non-standard display sizes and/or different focal length photographing modes
US5384912A (en) 1987-10-30 1995-01-24 New Microtime Inc. Real time video image processing system
US5430809A (en) 1992-07-10 1995-07-04 Sony Corporation Human face tracking system
US5432863A (en) 1993-07-19 1995-07-11 Eastman Kodak Company Automated detection and correction of eye color defects due to flash illumination
US5488429A (en) 1992-01-13 1996-01-30 Mitsubishi Denki Kabushiki Kaisha Video signal processor for detecting flesh tones in am image
US5496106A (en) 1994-12-13 1996-03-05 Apple Computer, Inc. System and method for generating a contrast overlay as a focus assist for an imaging device
US5572596A (en) 1994-09-02 1996-11-05 David Sarnoff Research Center, Inc. Automated, non-invasive iris recognition system and method
US5576759A (en) 1992-12-07 1996-11-19 Nikon Corporation Image processing system for classifying reduced image data
US5633678A (en) 1995-12-20 1997-05-27 Eastman Kodak Company Electronic still camera for capturing and categorizing images
US5638136A (en) 1992-01-13 1997-06-10 Mitsubishi Denki Kabushiki Kaisha Method and apparatus for detecting flesh tones in an image
US5680481A (en) 1992-05-26 1997-10-21 Ricoh Corporation Facial feature extraction method and apparatus for a neural network acoustic and visual speech recognition system
US5684509A (en) 1992-01-06 1997-11-04 Fuji Photo Film Co., Ltd. Method and apparatus for processing image
US5692065A (en) 1994-08-18 1997-11-25 International Business Machines Corporation Apparatus and method for determining image quality
US5706362A (en) 1993-03-31 1998-01-06 Mitsubishi Denki Kabushiki Kaisha Image tracking apparatus
US5710833A (en) 1995-04-20 1998-01-20 Massachusetts Institute Of Technology Detection, recognition and coding of complex objects using probabilistic eigenspace analysis
US5724456A (en) 1995-03-31 1998-03-03 Polaroid Corporation Brightness adjustment of images using digital scene analysis
US5774747A (en) 1994-06-09 1998-06-30 Fuji Photo Film Co., Ltd. Method and apparatus for controlling exposure of camera
US5774591A (en) 1995-12-15 1998-06-30 Xerox Corporation Apparatus and method for recognizing facial expressions and facial gestures in a sequence of images
US5774754A (en) 1994-04-26 1998-06-30 Minolta Co., Ltd. Camera capable of previewing a photographed image
US5781650A (en) 1994-02-18 1998-07-14 University Of Central Florida Automatic feature detection and age classification of human faces in digital images
US5802361A (en) 1994-09-30 1998-09-01 Apple Computer, Inc. Method and system for searching graphic images and videos
US5802220A (en) 1995-12-15 1998-09-01 Xerox Corporation Apparatus and method for tracking facial motion through a sequence of images
US5802208A (en) 1996-05-06 1998-09-01 Lucent Technologies Inc. Face recognition using DCT-based feature vectors
US5805720A (en) * 1995-07-28 1998-09-08 Mitsubishi Denki Kabushiki Kaisha Facial image processing system
US5812193A (en) 1992-11-07 1998-09-22 Sony Corporation Video camera system which automatically follows subject changes
US5818975A (en) 1996-10-28 1998-10-06 Eastman Kodak Company Method and apparatus for area selective exposure adjustment
US5835616A (en) 1994-02-18 1998-11-10 University Of Central Florida Face detection using templates
US5842194A (en) 1995-07-28 1998-11-24 Mitsubishi Denki Kabushiki Kaisha Method of recognizing images of faces or general images using fuzzy combination of multiple resolutions
US5870138A (en) 1995-03-31 1999-02-09 Hitachi, Ltd. Facial image processing
US5963656A (en) 1996-09-30 1999-10-05 International Business Machines Corporation System and method for determining the quality of fingerprint images
US5978519A (en) 1996-08-06 1999-11-02 Xerox Corporation Automatic image cropping
US5991456A (en) 1996-05-29 1999-11-23 Science And Technology Corporation Method of improving a digital image
US6053268A (en) 1997-01-23 2000-04-25 Nissan Motor Co., Ltd. Vehicle environment recognition system
US6072903A (en) 1997-01-07 2000-06-06 Kabushiki Kaisha Toshiba Image processing apparatus and image processing method
US6097470A (en) 1998-05-28 2000-08-01 Eastman Kodak Company Digital photofinishing system including scene balance, contrast normalization, and image sharpening digital image processing
US6101271A (en) 1990-10-09 2000-08-08 Matsushita Electrial Industrial Co., Ltd Gradation correction method and device
US6115509A (en) 1994-03-10 2000-09-05 International Business Machines Corp High volume document image archive system and method
US6128397A (en) 1997-11-21 2000-10-03 Justsystem Pittsburgh Research Center Method for finding all frontal faces in arbitrarily complex visual scenes
US6148092A (en) 1998-01-08 2000-11-14 Sharp Laboratories Of America, Inc System for detecting skin-tone regions within an image
US6151073A (en) 1996-03-28 2000-11-21 Fotonation, Inc. Intelligent camera flash system
US6188777B1 (en) 1997-08-01 2001-02-13 Interval Research Corporation Method and apparatus for personnel detection and tracking
US6192149B1 (en) 1998-04-08 2001-02-20 Xerox Corporation Method and apparatus for automatic detection of image target gamma
US6249315B1 (en) 1997-03-24 2001-06-19 Jack M. Holm Strategy for pictorial digital image processing
US6263113B1 (en) 1998-12-11 2001-07-17 Philips Electronics North America Corp. Method for detecting a face in a digital image
US6268939B1 (en) 1998-01-08 2001-07-31 Xerox Corporation Method and apparatus for correcting luminance and chrominance data in digital color images
US6282317B1 (en) 1998-12-31 2001-08-28 Eastman Kodak Company Method for automatic determination of main subjects in photographic images
US6301370B1 (en) 1998-04-13 2001-10-09 Eyematic Interfaces, Inc. Face recognition from video images
US6301440B1 (en) 2000-04-13 2001-10-09 International Business Machines Corp. System and method for automatically setting image acquisition controls
US20010028731A1 (en) 1996-05-21 2001-10-11 Michele Covell Canonical correlation analysis of image/control-point location coupling for the automatic location of control points
US20010031142A1 (en) 1999-12-20 2001-10-18 Whiteside George D. Scene recognition method and system using brightness and ranging mapping
US20010040987A1 (en) 1997-04-21 2001-11-15 Vance C. Bjorn Fingerprint recognition system
US6393148B1 (en) 1999-05-13 2002-05-21 Hewlett-Packard Company Contrast enhancement of an image using luminance and RGB statistical metrics
US6400830B1 (en) 1998-02-06 2002-06-04 Compaq Computer Corporation Technique for tracking objects through a series of images
US6404900B1 (en) 1998-06-22 2002-06-11 Sharp Laboratories Of America, Inc. Method for robust human face tracking in presence of multiple persons
US6407777B1 (en) 1997-10-09 2002-06-18 Deluca Michael Joseph Red-eye filter method and apparatus
GB2370438A (en) 2000-12-22 2002-06-26 Hewlett Packard Co Automated image cropping using selected compositional rules.
JP2002199202A (en) 2000-12-26 2002-07-12 Seiko Epson Corp Image processing apparatus
US6421468B1 (en) 1999-01-06 2002-07-16 Seiko Epson Corporation Method and apparatus for sharpening an image by scaling elements of a frequency-domain representation
US20020105662A1 (en) 1998-12-21 2002-08-08 Eastman Kodak Company Method and apparatus for modifying a portion of an image in accordance with colorimetric parameters
US6438264B1 (en) 1998-12-31 2002-08-20 Eastman Kodak Company Method for compensating image color when adjusting the contrast of a digital color image
US20020114535A1 (en) 2000-12-14 2002-08-22 Eastman Kodak Company Automatically producing an image of a portion of a photographic image
US6456737B1 (en) 1997-04-15 2002-09-24 Interval Research Corporation Data processing system and method
US6456732B1 (en) 1998-09-11 2002-09-24 Hewlett-Packard Company Automatic rotation, cropping and scaling of images for printing
US6459436B1 (en) 1998-11-11 2002-10-01 Canon Kabushiki Kaisha Image processing method and apparatus
US6473199B1 (en) 1998-12-18 2002-10-29 Eastman Kodak Company Correcting exposure and tone scale of digital images captured by an image capture device
US20020172419A1 (en) 2001-05-15 2002-11-21 Qian Lin Image enhancement using face detection
US6501857B1 (en) 1999-07-20 2002-12-31 Craig Gotsman Method and system for detecting and classifying objects in an image
US6504951B1 (en) 1999-11-29 2003-01-07 Eastman Kodak Company Method for detecting sky in images
US6504942B1 (en) 1998-01-23 2003-01-07 Sharp Kabushiki Kaisha Method of and apparatus for detecting a face-like region and observer tracking display
US6516154B1 (en) 2001-07-17 2003-02-04 Eastman Kodak Company Image revising camera and method
US20030025812A1 (en) 2001-07-10 2003-02-06 Slatter David Neil Intelligent feature selection and pan zoom control
US6526161B1 (en) 1999-08-30 2003-02-25 Koninklijke Philips Electronics N.V. System and method for biometrics-based facial feature extraction
US20030052991A1 (en) 2001-09-17 2003-03-20 Stavely Donald J. System and method for simulating fill flash in photography
US20030068100A1 (en) 2001-07-17 2003-04-10 Covell Michele M. Automatic selection of a visual image or images from a collection of visual images, based on an evaluation of the quality of the visual images
US20030071908A1 (en) 2001-09-18 2003-04-17 Masato Sannoh Image pickup device, automatic focusing method, automatic exposure method, electronic flash control method and computer program
US6556708B1 (en) 1998-02-06 2003-04-29 Compaq Computer Corporation Technique for classifying objects within an image
US6606398B2 (en) 1998-09-30 2003-08-12 Intel Corporation Automatic cataloging of people in digital photographs
US6606397B1 (en) 1999-05-25 2003-08-12 Mitsubishi Denki Kabushiki Kaisha Face image processing apparatus for extraction of an eye image based on the position of the naris
US20030160879A1 (en) 2002-02-28 2003-08-28 Robins Mark Nelson White eye portraiture system and method
US20030169906A1 (en) 2002-02-26 2003-09-11 Gokturk Salih Burak Method and apparatus for recognizing objects
US20030190090A1 (en) 2002-04-09 2003-10-09 Beeman Edward S. System and method for digital-image enhancement
US6633655B1 (en) 1998-09-05 2003-10-14 Sharp Kabushiki Kaisha Method of and apparatus for detecting a human face and observer tracking display
US6636694B1 (en) * 1999-09-14 2003-10-21 Kabushiki Kaisha Toshiba Face image photographing apparatus and face image photographing method
US6661907B2 (en) 1998-06-10 2003-12-09 Canon Kabushiki Kaisha Face detection in digital images
US20040001616A1 (en) 2002-06-27 2004-01-01 Srinivas Gutta Measurement of content ratings through vision and speech recognition
US6697503B2 (en) 1999-12-01 2004-02-24 Matsushita Electric Industrial Co., Ltd. Device and method for face image extraction, and recording medium having recorded program for the method
US6697504B2 (en) 2000-12-15 2004-02-24 Institute For Information Industry Method of multi-level facial image recognition and system using the same
US6754389B1 (en) 1999-12-01 2004-06-22 Koninklijke Philips Electronics N.V. Program classification using object tracking
US6760465B2 (en) 2001-03-30 2004-07-06 Intel Corporation Mechanism for tracking colored objects in a video sequence
US6765612B1 (en) 1996-12-09 2004-07-20 Flashpoint Technology, Inc. Method and system for naming images captured by a digital camera
US20040170397A1 (en) 1999-06-03 2004-09-02 Fuji Photo Film Co., Ltd. Camera and method of photographing good image
US6801250B1 (en) 1999-09-10 2004-10-05 Sony Corporation Converting a multi-pixel image to a reduced-pixel image to provide an output image with improved image quality
US20040213482A1 (en) 1997-07-12 2004-10-28 Kia Silverbrook Method of capturing and processing sensed images
US20040223629A1 (en) 2003-05-06 2004-11-11 Viswis, Inc. Facial surveillance system and method
US20040258304A1 (en) 2003-01-31 2004-12-23 Kazuo Shiota Apparatus and program for selecting photographic images
JP2005003852A (en) 2003-06-11 2005-01-06 Nikon Corp Automatic photographing device
US20050013479A1 (en) 2003-07-16 2005-01-20 Rong Xiao Robust multi-view face detection methods and apparatuses
US6850274B1 (en) 1997-07-15 2005-02-01 Silverbrook Research Pty Ltd Image texture mapping camera
US20050069208A1 (en) 2003-08-29 2005-03-31 Sony Corporation Object detector, object detecting method and robot
US6876755B1 (en) 1998-12-02 2005-04-05 The University Of Manchester Face sub-space determination
US6879705B1 (en) 1999-07-14 2005-04-12 Sarnoff Corporation Method and apparatus for tracking multiple objects in a video sequence
US6940545B1 (en) 2000-02-28 2005-09-06 Eastman Kodak Company Face detecting camera and method
US6965684B2 (en) 2000-09-15 2005-11-15 Canon Kabushiki Kaisha Image processing methods and apparatus for detecting human eyes, human face, and other objects in an image
US20050286802A1 (en) 2004-06-22 2005-12-29 Canon Kabushiki Kaisha Method for detecting and selecting good quality image frames from video
US6993157B1 (en) 1999-05-18 2006-01-31 Sanyo Electric Co., Ltd. Dynamic image processing method and device and medium
US7003135B2 (en) 2001-05-25 2006-02-21 Industrial Technology Research Institute System and method for rapidly tracking multiple faces
US7020337B2 (en) 2002-07-22 2006-03-28 Mitsubishi Electric Research Laboratories, Inc. System and method for detecting objects in images
US7027619B2 (en) 2001-09-13 2006-04-11 Honeywell International Inc. Near-infrared method and system for use in face detection
US7035467B2 (en) 2002-01-09 2006-04-25 Eastman Kodak Company Method and system for processing images for themed imaging services
US7035456B2 (en) 2001-06-01 2006-04-25 Canon Kabushiki Kaisha Face detection in color images with complex background
US7035440B2 (en) 2000-07-03 2006-04-25 Fuji Photo Film Co., Ltd. Image collecting system and method thereof
US7038715B1 (en) 1999-01-19 2006-05-02 Texas Instruments Incorporated Digital still camera with high-quality portrait mode
US7038709B1 (en) 2000-11-01 2006-05-02 Gilbert Verghese System and method for tracking a subject
US7050607B2 (en) 2001-12-08 2006-05-23 Microsoft Corp. System and method for multi-view face detection
US7064776B2 (en) 2001-05-09 2006-06-20 National Institute Of Advanced Industrial Science And Technology Object tracking apparatus, object tracking method and recording medium
US7082212B2 (en) 2000-03-09 2006-07-25 Microsoft Corporation Rapid computer modeling of faces for animation
US20060177131A1 (en) 2005-02-07 2006-08-10 Porikli Fatih M Method of extracting and searching integral histograms of data samples
US20060177100A1 (en) 2005-02-09 2006-08-10 Ying Zhu System and method for detecting features from images of vehicles
US7099510B2 (en) 2000-11-29 2006-08-29 Hewlett-Packard Development Company, L.P. Method and system for object detection in digital images
US20060204106A1 (en) 2005-03-11 2006-09-14 Fuji Photo Film Co., Ltd. Imaging device, imaging method and imaging program
US7110575B2 (en) 2002-08-02 2006-09-19 Eastman Kodak Company Method for locating faces in digital color images
US7113641B1 (en) 1998-08-14 2006-09-26 Christian Eckes Method for recognizing objects in digitized images
US7120279B2 (en) 2003-01-30 2006-10-10 Eastman Kodak Company Method for face orientation determination in digital color images
US7119838B2 (en) 2004-08-19 2006-10-10 Blue Marlin Llc Method and imager for detecting the location of objects
US7151843B2 (en) 2001-12-03 2006-12-19 Microsoft Corporation Automatic detection and tracking of multiple individuals using multiple cues
US7158680B2 (en) 2004-07-30 2007-01-02 Euclid Discoveries, Llc Apparatus and method for processing video data
US7162101B2 (en) 2001-11-15 2007-01-09 Canon Kabushiki Kaisha Image processing apparatus and method
US7162076B2 (en) 2003-02-11 2007-01-09 New Jersey Institute Of Technology Face detection method and apparatus
US7171023B2 (en) 2001-11-05 2007-01-30 Samsung Electronics Co., Ltd. Illumination-invariant object tracking method and image editing system using the same
EP1748378A1 (en) 2005-07-26 2007-01-31 Canon Kabushiki Kaisha Image capturing apparatus and image capturing method
US7174033B2 (en) 2002-05-22 2007-02-06 A4Vision Methods and systems for detecting and recognizing an object based on 3D image data
US7190829B2 (en) 2003-06-30 2007-03-13 Microsoft Corporation Speedup of face detection in digital images
US7200249B2 (en) 2000-11-17 2007-04-03 Sony Corporation Robot device and face identifying method, and image identifying device and image identifying method
US20070091203A1 (en) 2005-10-25 2007-04-26 Peker Kadir A Method and system for segmenting videos using face detection
US20070098303A1 (en) 2005-10-31 2007-05-03 Eastman Kodak Company Determining a particular person from a collection
WO2007060980A1 (en) 2005-11-25 2007-05-31 Nikon Corporation Electronic camera and image processing device
US7227976B1 (en) 2002-07-08 2007-06-05 Videomining Corporation Method and system for real-time facial image enhancement
US7233684B2 (en) 2002-11-25 2007-06-19 Eastman Kodak Company Imaging method and system using affective information
US20070154096A1 (en) 2005-12-31 2007-07-05 Jiangen Cao Facial feature detection on mobile devices
US20070154095A1 (en) 2005-12-31 2007-07-05 Arcsoft, Inc. Face detection on mobile devices
US7254257B2 (en) 2002-03-04 2007-08-07 Samsung Electronics Co., Ltd. Method and apparatus of recognizing face using component-based 2nd-order principal component analysis (PCA)/independent component analysis (ICA)
US7274832B2 (en) 2003-11-13 2007-09-25 Eastman Kodak Company In-plane rotation invariant object detection in digitized images
US7274822B2 (en) 2003-06-30 2007-09-25 Microsoft Corporation Face annotation for photo management
US7317815B2 (en) 2003-06-26 2008-01-08 Fotonation Vision Limited Digital image processing composition using face detection information
US20080025576A1 (en) 2006-07-25 2008-01-31 Arcsoft, Inc. Method for detecting facial expressions of a portrait photo by an image capturing electronic device
US20080144966A1 (en) 2003-09-30 2008-06-19 Fotonation Vision Limited Automated Statistical Self-Calibrating Detection and Removal of Blemishes in Digital Images Based on Determining Probabilities Based on Image Analysis of Single Images
US20080192129A1 (en) 2003-12-24 2008-08-14 Walker Jay S Method and Apparatus for Automatically Capturing and Managing Images
US7551755B1 (en) 2004-01-22 2009-06-23 Fotonation Vision Limited Classification and organization of consumer digital images using workflow, and face detection and recognition
US7551754B2 (en) 2006-02-24 2009-06-23 Fotonation Vision Limited Method and apparatus for selective rejection of digital images
US7715597B2 (en) 2004-12-29 2010-05-11 Fotonation Ireland Limited Method and component for image recognition
US7738015B2 (en) 1997-10-09 2010-06-15 Fotonation Vision Limited Red-eye filter method and apparatus
US7804983B2 (en) 2006-02-24 2010-09-28 Fotonation Vision Limited Digital image acquisition control and correction method and apparatus

Family Cites Families (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JP3970725B2 (en) * 2002-09-11 2007-09-05 本田技研工業株式会社 Engine fuel injection system
KR100459438B1 (en) * 2002-10-02 2004-12-03 엘지전자 주식회사 Capture method for still image of camera
WO2004055715A1 (en) * 2002-12-13 2004-07-01 Koninklijke Philips Electronics N.V. Expression invariant face recognition

Patent Citations (171)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
USRE31370E (en) 1974-04-01 1983-09-06 Canon Kabushiki Kaisha System for exposure measurement and/or focus detection by means of image sensor
US4047187A (en) 1974-04-01 1977-09-06 Canon Kabushiki Kaisha System for exposure measurement and/or focus detection by means of image senser
US4317991A (en) 1980-03-12 1982-03-02 Honeywell Inc. Digital auto focus system utilizing a photodetector array
US4367027A (en) 1980-03-12 1983-01-04 Honeywell Inc. Active auto focus system improvement
US4299464A (en) * 1980-08-04 1981-11-10 Eastman Kodak Company Method and apparatus for reducing the incidence of eye closures during photographing of a human subject
US4638364A (en) 1984-10-30 1987-01-20 Sanyo Electric Co., Ltd. Auto focus circuit for video camera
USRE33682E (en) 1984-10-30 1991-09-03 Sanyo Electric Co., Ltd. Auto focus circuit for video camera
US5291234A (en) 1987-02-04 1994-03-01 Asahi Kogaku Kogyo Kabushiki Kaisha Auto optical focus detecting device and eye direction detecting optical system
US5384912A (en) 1987-10-30 1995-01-24 New Microtime Inc. Real time video image processing system
US5018017A (en) 1987-12-25 1991-05-21 Kabushiki Kaisha Toshiba Electronic still camera and image recording method thereof
US5227837A (en) 1989-05-12 1993-07-13 Fuji Photo Film Co., Ltd. Photograph printing method
US5063603A (en) 1989-11-06 1991-11-05 David Sarnoff Research Center, Inc. Dynamic method for recognizing objects and image processing system therefor
US5164831A (en) 1990-03-15 1992-11-17 Eastman Kodak Company Electronic still camera providing multi-format storage of full and reduced resolution images
US5280530A (en) 1990-09-07 1994-01-18 U.S. Philips Corporation Method and apparatus for tracking a moving object
US6101271A (en) 1990-10-09 2000-08-08 Matsushita Electrial Industrial Co., Ltd Gradation correction method and device
US5164992A (en) 1990-11-01 1992-11-17 Massachusetts Institute Of Technology Face recognition system
US5684509A (en) 1992-01-06 1997-11-04 Fuji Photo Film Co., Ltd. Method and apparatus for processing image
US5638136A (en) 1992-01-13 1997-06-10 Mitsubishi Denki Kabushiki Kaisha Method and apparatus for detecting flesh tones in an image
US5488429A (en) 1992-01-13 1996-01-30 Mitsubishi Denki Kabushiki Kaisha Video signal processor for detecting flesh tones in am image
US5680481A (en) 1992-05-26 1997-10-21 Ricoh Corporation Facial feature extraction method and apparatus for a neural network acoustic and visual speech recognition system
US5430809A (en) 1992-07-10 1995-07-04 Sony Corporation Human face tracking system
US5311240A (en) 1992-11-03 1994-05-10 Eastman Kodak Company Technique suited for use in multi-zone autofocusing cameras for improving image quality for non-standard display sizes and/or different focal length photographing modes
US5812193A (en) 1992-11-07 1998-09-22 Sony Corporation Video camera system which automatically follows subject changes
US5576759A (en) 1992-12-07 1996-11-19 Nikon Corporation Image processing system for classifying reduced image data
US5706362A (en) 1993-03-31 1998-01-06 Mitsubishi Denki Kabushiki Kaisha Image tracking apparatus
US5432863A (en) 1993-07-19 1995-07-11 Eastman Kodak Company Automated detection and correction of eye color defects due to flash illumination
US5835616A (en) 1994-02-18 1998-11-10 University Of Central Florida Face detection using templates
US5781650A (en) 1994-02-18 1998-07-14 University Of Central Florida Automatic feature detection and age classification of human faces in digital images
US6115509A (en) 1994-03-10 2000-09-05 International Business Machines Corp High volume document image archive system and method
US5774754A (en) 1994-04-26 1998-06-30 Minolta Co., Ltd. Camera capable of previewing a photographed image
US5774747A (en) 1994-06-09 1998-06-30 Fuji Photo Film Co., Ltd. Method and apparatus for controlling exposure of camera
US5692065A (en) 1994-08-18 1997-11-25 International Business Machines Corporation Apparatus and method for determining image quality
US5572596A (en) 1994-09-02 1996-11-05 David Sarnoff Research Center, Inc. Automated, non-invasive iris recognition system and method
US5802361A (en) 1994-09-30 1998-09-01 Apple Computer, Inc. Method and system for searching graphic images and videos
US5496106A (en) 1994-12-13 1996-03-05 Apple Computer, Inc. System and method for generating a contrast overlay as a focus assist for an imaging device
US5724456A (en) 1995-03-31 1998-03-03 Polaroid Corporation Brightness adjustment of images using digital scene analysis
US5870138A (en) 1995-03-31 1999-02-09 Hitachi, Ltd. Facial image processing
US5710833A (en) 1995-04-20 1998-01-20 Massachusetts Institute Of Technology Detection, recognition and coding of complex objects using probabilistic eigenspace analysis
US5805720A (en) * 1995-07-28 1998-09-08 Mitsubishi Denki Kabushiki Kaisha Facial image processing system
US5842194A (en) 1995-07-28 1998-11-24 Mitsubishi Denki Kabushiki Kaisha Method of recognizing images of faces or general images using fuzzy combination of multiple resolutions
US5774591A (en) 1995-12-15 1998-06-30 Xerox Corporation Apparatus and method for recognizing facial expressions and facial gestures in a sequence of images
US5802220A (en) 1995-12-15 1998-09-01 Xerox Corporation Apparatus and method for tracking facial motion through a sequence of images
US5633678A (en) 1995-12-20 1997-05-27 Eastman Kodak Company Electronic still camera for capturing and categorizing images
US6151073A (en) 1996-03-28 2000-11-21 Fotonation, Inc. Intelligent camera flash system
US5802208A (en) 1996-05-06 1998-09-01 Lucent Technologies Inc. Face recognition using DCT-based feature vectors
US20010028731A1 (en) 1996-05-21 2001-10-11 Michele Covell Canonical correlation analysis of image/control-point location coupling for the automatic location of control points
US5991456A (en) 1996-05-29 1999-11-23 Science And Technology Corporation Method of improving a digital image
US5978519A (en) 1996-08-06 1999-11-02 Xerox Corporation Automatic image cropping
US5963656A (en) 1996-09-30 1999-10-05 International Business Machines Corporation System and method for determining the quality of fingerprint images
US5818975A (en) 1996-10-28 1998-10-06 Eastman Kodak Company Method and apparatus for area selective exposure adjustment
US6765612B1 (en) 1996-12-09 2004-07-20 Flashpoint Technology, Inc. Method and system for naming images captured by a digital camera
US6072903A (en) 1997-01-07 2000-06-06 Kabushiki Kaisha Toshiba Image processing apparatus and image processing method
US6053268A (en) 1997-01-23 2000-04-25 Nissan Motor Co., Ltd. Vehicle environment recognition system
US6249315B1 (en) 1997-03-24 2001-06-19 Jack M. Holm Strategy for pictorial digital image processing
US6456737B1 (en) 1997-04-15 2002-09-24 Interval Research Corporation Data processing system and method
US20010040987A1 (en) 1997-04-21 2001-11-15 Vance C. Bjorn Fingerprint recognition system
US20040213482A1 (en) 1997-07-12 2004-10-28 Kia Silverbrook Method of capturing and processing sensed images
US6850274B1 (en) 1997-07-15 2005-02-01 Silverbrook Research Pty Ltd Image texture mapping camera
US6188777B1 (en) 1997-08-01 2001-02-13 Interval Research Corporation Method and apparatus for personnel detection and tracking
US7738015B2 (en) 1997-10-09 2010-06-15 Fotonation Vision Limited Red-eye filter method and apparatus
US6407777B1 (en) 1997-10-09 2002-06-18 Deluca Michael Joseph Red-eye filter method and apparatus
US6128397A (en) 1997-11-21 2000-10-03 Justsystem Pittsburgh Research Center Method for finding all frontal faces in arbitrarily complex visual scenes
US6332033B1 (en) 1998-01-08 2001-12-18 Sharp Laboratories Of America, Inc. System for detecting skin-tone regions within an image
US6148092A (en) 1998-01-08 2000-11-14 Sharp Laboratories Of America, Inc System for detecting skin-tone regions within an image
US6268939B1 (en) 1998-01-08 2001-07-31 Xerox Corporation Method and apparatus for correcting luminance and chrominance data in digital color images
US6504942B1 (en) 1998-01-23 2003-01-07 Sharp Kabushiki Kaisha Method of and apparatus for detecting a face-like region and observer tracking display
US6556708B1 (en) 1998-02-06 2003-04-29 Compaq Computer Corporation Technique for classifying objects within an image
US6400830B1 (en) 1998-02-06 2002-06-04 Compaq Computer Corporation Technique for tracking objects through a series of images
US6192149B1 (en) 1998-04-08 2001-02-20 Xerox Corporation Method and apparatus for automatic detection of image target gamma
US6301370B1 (en) 1998-04-13 2001-10-09 Eyematic Interfaces, Inc. Face recognition from video images
US6097470A (en) 1998-05-28 2000-08-01 Eastman Kodak Company Digital photofinishing system including scene balance, contrast normalization, and image sharpening digital image processing
US7218759B1 (en) 1998-06-10 2007-05-15 Canon Kabushiki Kaisha Face detection in digital images
US6661907B2 (en) 1998-06-10 2003-12-09 Canon Kabushiki Kaisha Face detection in digital images
US6404900B1 (en) 1998-06-22 2002-06-11 Sharp Laboratories Of America, Inc. Method for robust human face tracking in presence of multiple persons
US7113641B1 (en) 1998-08-14 2006-09-26 Christian Eckes Method for recognizing objects in digitized images
US6633655B1 (en) 1998-09-05 2003-10-14 Sharp Kabushiki Kaisha Method of and apparatus for detecting a human face and observer tracking display
US6456732B1 (en) 1998-09-11 2002-09-24 Hewlett-Packard Company Automatic rotation, cropping and scaling of images for printing
US6606398B2 (en) 1998-09-30 2003-08-12 Intel Corporation Automatic cataloging of people in digital photographs
US6459436B1 (en) 1998-11-11 2002-10-01 Canon Kabushiki Kaisha Image processing method and apparatus
US6876755B1 (en) 1998-12-02 2005-04-05 The University Of Manchester Face sub-space determination
US6263113B1 (en) 1998-12-11 2001-07-17 Philips Electronics North America Corp. Method for detecting a face in a digital image
US6473199B1 (en) 1998-12-18 2002-10-29 Eastman Kodak Company Correcting exposure and tone scale of digital images captured by an image capture device
US20020105662A1 (en) 1998-12-21 2002-08-08 Eastman Kodak Company Method and apparatus for modifying a portion of an image in accordance with colorimetric parameters
US6438264B1 (en) 1998-12-31 2002-08-20 Eastman Kodak Company Method for compensating image color when adjusting the contrast of a digital color image
US6282317B1 (en) 1998-12-31 2001-08-28 Eastman Kodak Company Method for automatic determination of main subjects in photographic images
US6421468B1 (en) 1999-01-06 2002-07-16 Seiko Epson Corporation Method and apparatus for sharpening an image by scaling elements of a frequency-domain representation
US7038715B1 (en) 1999-01-19 2006-05-02 Texas Instruments Incorporated Digital still camera with high-quality portrait mode
US6393148B1 (en) 1999-05-13 2002-05-21 Hewlett-Packard Company Contrast enhancement of an image using luminance and RGB statistical metrics
US6993157B1 (en) 1999-05-18 2006-01-31 Sanyo Electric Co., Ltd. Dynamic image processing method and device and medium
US6606397B1 (en) 1999-05-25 2003-08-12 Mitsubishi Denki Kabushiki Kaisha Face image processing apparatus for extraction of an eye image based on the position of the naris
US20040170397A1 (en) 1999-06-03 2004-09-02 Fuji Photo Film Co., Ltd. Camera and method of photographing good image
US6879705B1 (en) 1999-07-14 2005-04-12 Sarnoff Corporation Method and apparatus for tracking multiple objects in a video sequence
US6501857B1 (en) 1999-07-20 2002-12-31 Craig Gotsman Method and system for detecting and classifying objects in an image
US6526161B1 (en) 1999-08-30 2003-02-25 Koninklijke Philips Electronics N.V. System and method for biometrics-based facial feature extraction
US6801250B1 (en) 1999-09-10 2004-10-05 Sony Corporation Converting a multi-pixel image to a reduced-pixel image to provide an output image with improved image quality
US6636694B1 (en) * 1999-09-14 2003-10-21 Kabushiki Kaisha Toshiba Face image photographing apparatus and face image photographing method
US6504951B1 (en) 1999-11-29 2003-01-07 Eastman Kodak Company Method for detecting sky in images
US6697503B2 (en) 1999-12-01 2004-02-24 Matsushita Electric Industrial Co., Ltd. Device and method for face image extraction, and recording medium having recorded program for the method
US6754389B1 (en) 1999-12-01 2004-06-22 Koninklijke Philips Electronics N.V. Program classification using object tracking
US20010031142A1 (en) 1999-12-20 2001-10-18 Whiteside George D. Scene recognition method and system using brightness and ranging mapping
US6940545B1 (en) 2000-02-28 2005-09-06 Eastman Kodak Company Face detecting camera and method
US7082212B2 (en) 2000-03-09 2006-07-25 Microsoft Corporation Rapid computer modeling of faces for animation
US6301440B1 (en) 2000-04-13 2001-10-09 International Business Machines Corp. System and method for automatically setting image acquisition controls
US7035440B2 (en) 2000-07-03 2006-04-25 Fuji Photo Film Co., Ltd. Image collecting system and method thereof
US6965684B2 (en) 2000-09-15 2005-11-15 Canon Kabushiki Kaisha Image processing methods and apparatus for detecting human eyes, human face, and other objects in an image
US7038709B1 (en) 2000-11-01 2006-05-02 Gilbert Verghese System and method for tracking a subject
US7200249B2 (en) 2000-11-17 2007-04-03 Sony Corporation Robot device and face identifying method, and image identifying device and image identifying method
US7099510B2 (en) 2000-11-29 2006-08-29 Hewlett-Packard Development Company, L.P. Method and system for object detection in digital images
US20020114535A1 (en) 2000-12-14 2002-08-22 Eastman Kodak Company Automatically producing an image of a portion of a photographic image
US6697504B2 (en) 2000-12-15 2004-02-24 Institute For Information Industry Method of multi-level facial image recognition and system using the same
GB2370438A (en) 2000-12-22 2002-06-26 Hewlett Packard Co Automated image cropping using selected compositional rules.
JP2002199202A (en) 2000-12-26 2002-07-12 Seiko Epson Corp Image processing apparatus
US6760465B2 (en) 2001-03-30 2004-07-06 Intel Corporation Mechanism for tracking colored objects in a video sequence
US7064776B2 (en) 2001-05-09 2006-06-20 National Institute Of Advanced Industrial Science And Technology Object tracking apparatus, object tracking method and recording medium
US20020172419A1 (en) 2001-05-15 2002-11-21 Qian Lin Image enhancement using face detection
US7003135B2 (en) 2001-05-25 2006-02-21 Industrial Technology Research Institute System and method for rapidly tracking multiple faces
US7035456B2 (en) 2001-06-01 2006-04-25 Canon Kabushiki Kaisha Face detection in color images with complex background
US20030025812A1 (en) 2001-07-10 2003-02-06 Slatter David Neil Intelligent feature selection and pan zoom control
US6516154B1 (en) 2001-07-17 2003-02-04 Eastman Kodak Company Image revising camera and method
US20030068100A1 (en) 2001-07-17 2003-04-10 Covell Michele M. Automatic selection of a visual image or images from a collection of visual images, based on an evaluation of the quality of the visual images
US7027619B2 (en) 2001-09-13 2006-04-11 Honeywell International Inc. Near-infrared method and system for use in face detection
US20030052991A1 (en) 2001-09-17 2003-03-20 Stavely Donald J. System and method for simulating fill flash in photography
US20030071908A1 (en) 2001-09-18 2003-04-17 Masato Sannoh Image pickup device, automatic focusing method, automatic exposure method, electronic flash control method and computer program
US7171023B2 (en) 2001-11-05 2007-01-30 Samsung Electronics Co., Ltd. Illumination-invariant object tracking method and image editing system using the same
US7162101B2 (en) 2001-11-15 2007-01-09 Canon Kabushiki Kaisha Image processing apparatus and method
US7151843B2 (en) 2001-12-03 2006-12-19 Microsoft Corporation Automatic detection and tracking of multiple individuals using multiple cues
US7171025B2 (en) 2001-12-03 2007-01-30 Microsoft Corporation Automatic detection and tracking of multiple individuals using multiple cues
US7050607B2 (en) 2001-12-08 2006-05-23 Microsoft Corp. System and method for multi-view face detection
US7035467B2 (en) 2002-01-09 2006-04-25 Eastman Kodak Company Method and system for processing images for themed imaging services
US20030169906A1 (en) 2002-02-26 2003-09-11 Gokturk Salih Burak Method and apparatus for recognizing objects
US20030160879A1 (en) 2002-02-28 2003-08-28 Robins Mark Nelson White eye portraiture system and method
US7254257B2 (en) 2002-03-04 2007-08-07 Samsung Electronics Co., Ltd. Method and apparatus of recognizing face using component-based 2nd-order principal component analysis (PCA)/independent component analysis (ICA)
US20030190090A1 (en) 2002-04-09 2003-10-09 Beeman Edward S. System and method for digital-image enhancement
US7174033B2 (en) 2002-05-22 2007-02-06 A4Vision Methods and systems for detecting and recognizing an object based on 3D image data
US20040001616A1 (en) 2002-06-27 2004-01-01 Srinivas Gutta Measurement of content ratings through vision and speech recognition
US7227976B1 (en) 2002-07-08 2007-06-05 Videomining Corporation Method and system for real-time facial image enhancement
US7020337B2 (en) 2002-07-22 2006-03-28 Mitsubishi Electric Research Laboratories, Inc. System and method for detecting objects in images
US7110575B2 (en) 2002-08-02 2006-09-19 Eastman Kodak Company Method for locating faces in digital color images
US7233684B2 (en) 2002-11-25 2007-06-19 Eastman Kodak Company Imaging method and system using affective information
US7120279B2 (en) 2003-01-30 2006-10-10 Eastman Kodak Company Method for face orientation determination in digital color images
US20040258304A1 (en) 2003-01-31 2004-12-23 Kazuo Shiota Apparatus and program for selecting photographic images
US7162076B2 (en) 2003-02-11 2007-01-09 New Jersey Institute Of Technology Face detection method and apparatus
US20040223629A1 (en) 2003-05-06 2004-11-11 Viswis, Inc. Facial surveillance system and method
JP2005003852A (en) 2003-06-11 2005-01-06 Nikon Corp Automatic photographing device
US7317815B2 (en) 2003-06-26 2008-01-08 Fotonation Vision Limited Digital image processing composition using face detection information
US7190829B2 (en) 2003-06-30 2007-03-13 Microsoft Corporation Speedup of face detection in digital images
US7274822B2 (en) 2003-06-30 2007-09-25 Microsoft Corporation Face annotation for photo management
US20050013479A1 (en) 2003-07-16 2005-01-20 Rong Xiao Robust multi-view face detection methods and apparatuses
US20050069208A1 (en) 2003-08-29 2005-03-31 Sony Corporation Object detector, object detecting method and robot
US20080144966A1 (en) 2003-09-30 2008-06-19 Fotonation Vision Limited Automated Statistical Self-Calibrating Detection and Removal of Blemishes in Digital Images Based on Determining Probabilities Based on Image Analysis of Single Images
US7274832B2 (en) 2003-11-13 2007-09-25 Eastman Kodak Company In-plane rotation invariant object detection in digitized images
US20080192129A1 (en) 2003-12-24 2008-08-14 Walker Jay S Method and Apparatus for Automatically Capturing and Managing Images
US7551755B1 (en) 2004-01-22 2009-06-23 Fotonation Vision Limited Classification and organization of consumer digital images using workflow, and face detection and recognition
US20050286802A1 (en) 2004-06-22 2005-12-29 Canon Kabushiki Kaisha Method for detecting and selecting good quality image frames from video
US7158680B2 (en) 2004-07-30 2007-01-02 Euclid Discoveries, Llc Apparatus and method for processing video data
US7119838B2 (en) 2004-08-19 2006-10-10 Blue Marlin Llc Method and imager for detecting the location of objects
US7715597B2 (en) 2004-12-29 2010-05-11 Fotonation Ireland Limited Method and component for image recognition
US20060177131A1 (en) 2005-02-07 2006-08-10 Porikli Fatih M Method of extracting and searching integral histograms of data samples
US20060177100A1 (en) 2005-02-09 2006-08-10 Ying Zhu System and method for detecting features from images of vehicles
US20060204106A1 (en) 2005-03-11 2006-09-14 Fuji Photo Film Co., Ltd. Imaging device, imaging method and imaging program
US20070025722A1 (en) 2005-07-26 2007-02-01 Canon Kabushiki Kaisha Image capturing apparatus and image capturing method
EP1748378A1 (en) 2005-07-26 2007-01-31 Canon Kabushiki Kaisha Image capturing apparatus and image capturing method
US20070091203A1 (en) 2005-10-25 2007-04-26 Peker Kadir A Method and system for segmenting videos using face detection
US20070098303A1 (en) 2005-10-31 2007-05-03 Eastman Kodak Company Determining a particular person from a collection
WO2007060980A1 (en) 2005-11-25 2007-05-31 Nikon Corporation Electronic camera and image processing device
US20070154095A1 (en) 2005-12-31 2007-07-05 Arcsoft, Inc. Face detection on mobile devices
US20070154096A1 (en) 2005-12-31 2007-07-05 Jiangen Cao Facial feature detection on mobile devices
US7551754B2 (en) 2006-02-24 2009-06-23 Fotonation Vision Limited Method and apparatus for selective rejection of digital images
US7804983B2 (en) 2006-02-24 2010-09-28 Fotonation Vision Limited Digital image acquisition control and correction method and apparatus
US8005268B2 (en) * 2006-02-24 2011-08-23 Tessera Technologies Ireland Limited Digital image acquisition control and correction method and apparatus
US20080025576A1 (en) 2006-07-25 2008-01-31 Arcsoft, Inc. Method for detecting facial expressions of a portrait photo by an image capturing electronic device

Non-Patent Citations (32)

* Cited by examiner, † Cited by third party
Title
Bradski Gary et al., "Learning-Based Computer Vision with Intel's Open Source Computer Vision Library", Intel Technology, 2005, pp. 119-130, vol. 9-Issue 2.
Communication pursuant to Article 94(3) EPC, for European patent application No. 06789329.7, dated May 23, 2011, 5 pages.
Corcoran, P. et al., "Automatic Indexing of Consumer Image Collections Using Person Recognition Techniques", Digest of Technical Papers. International Conference on Consumer Electronics, 2005, pp. 127-128.
Costache, G. et al., "In-Camera Person-Indexing of Digital Images", Digest of Technical Papers. International Conference on Consumer Electronics, 2006, pp. 339-340.
Demirkir, C. et al., "Face detection using boosted tree classifier stages", Proceedings of the IEEE 12th Signal Processing and Communications Applications Conference, 2004, pp. 575-578.
Drimbarean, A.F. et al., "Image Processing Techniques to Detect and Filter Objectionable Images based on Skin Tone and Shape Recognition", International Conference on Consumer Electronics, 2001, pp. 278-279.
Examination Report for European Patent Application No. 06789329.7, dated Jul. 31, 2009, 5 Pages.
Final Office Action mailed Mar. 24, 2010, for U.S. Appl. No. 11/460,225, filed Jul. 26, 2006.
Huang W., et al., "Eye Tracking with Statistical Learning and Sequential Monte Carlo Sampling," Proceedings of the Fourth International Conference on Information, Communications & Signal Processing and Fourth IEEE Pacific-Rim Conference on Multimedia (ICICS-PCM2003), 2003, vol. 3, pp. 1873-1878.
Non-Final Office Action mailed Mar. 10, 2011, for U.S. Appl. No. 12/851,333, filed Aug. 5, 2010.
Non-Final Office Action mailed Mar. 25, 2010, for U.S. Appl. No. 11/460,218, filed Jul. 26, 2006.
Non-Final Office Action mailed Sep. 21, 2009, for U.S. Appl. No. 11/460,218, filed Jul. 26, 2006.
Non-Final Office Action mailed Sep. 22, 2009, for U.S. Appl. No. 11/460,225, filed Jul. 26, 2006.
Notice of Allowance mailed Aug. 20, 2010, for U.S. Appl. No. 11/460,225, filed Jul. 26, 2006.
Notice of Allowance mailed Jul. 13, 2010, for U.S. Appl. No. 11/460,225, filed Jul. 26, 2006.
Notice of Allowance mailed Jul. 14, 2011 for U.S. Appl. No. 12/851,333, filed Aug. 5, 2010.
Notice of Allowance mailed Jun. 28, 2011 for U.S. Appl. No. 12/851,333, filed Aug. 5, 2010.
Notice of Allowance mailed Jun. 29, 2010, for U.S. Appl. No. 11/460,218, filed Jul. 26, 2006.
PCT International Search Report and Written Opinion for Application No. PCT/EP2009/000315, dated Apr. 29, 2009, 11 pages.
PCT Notification Concerning Transmittal of International Preliminary Report on Patentability, PCT Appln. No. PCT/US2006/30173, dated Sep. 4, 2008, 7 pgs.
PCT Notification of Transmittal of the International Search Report and the Written Opinion of the International Searching Authority, or the Declaration, PCT Application No. PCT/US2006/030315, dated May 2, 2007.
PCT Notification of Transmittal of the Intl. Search Report and Written Opinion of the Intl. Searching Authority, PCT Appin. No. PCT/US2006/30173, dated Nov. 1, 2007, 12 pgs.
Rowley, Henry A. et al., "Neural network-based face detection, ISSN: 0162-8828, DOI: 10.1109/34.655647, Posted online: Aug. 6, 2002. http://ieeexplore.ieee.org/xpl/freeabs-all.jsp?arnumber-655647andisnumber-14286", IEEE Transactions on Pattern Analysis and Machine Intelligence, 1998, pp. 23-38, p. 92, vol. 20-Issue 1.
Supplementary European Search Report for Application No. 06789329, mailed on Jan. 13, 2009, 3 pages.
The extended European search report includes, pursuant to Rule 62 EPC, the supplementary European search report (Art. 153(7) EPC) and the European search opinion, for European application No. 06789329.7, dated Jan. 22, 2009, 7 pages.
The extended European search report includes, pursuant to Rule 62 EPC, the supplementary European search report (Art. 153(7) EPC) and the European search opinion, for European application No. 06800683.2, dated May 18, 2011, 7 pages.
Viola, P. et al., "Rapid Object Detection using a Boosted Cascade of Simple Features", Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2001, pp. I-511-I-518, vol. 1.
Viola, P. et al., "Robust Real-Time Face Detection", International Journal of Computer Vision, 2004, pp. 137-154, vol. 57-Issue 2, Kluwer Academic Publishers.
Xin He et al., "Real-Time Human Face Detection in Color Image", International Conference on Machine Learning and Cybernetics, 2003, pp. 2915-2920, vol. 5.
Yang, Ming-Hsuan et al., Detecting Faces in Images: A Survey, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 24-Issue 1, Jan. 1, 2002, pp. 34-58, IEEE Computer Society ISSN:0162-8828, http://portal.acm.org/citation.cfm?id=505621&coll=Guide&dl=Guide&CFID=680-9268&CFTOKEN=82843223.
Zhao, W. et al., "Face recognition: A literature survey, ISSN: 0360-0300, http://portal.acm.org/citation.cfm?id=954342&coll=GUIDE&dl=GUIDE&CFID=680-9268&CFTOKEN=82843223.", ACM Computing Surveys (CSUR) archive, 2003, pp. 399-458, vol. 35-Issue 4, ACM Press.
Zhu Qiang et al., "Fast Human Detection Using a Cascade of Histograms of Oriented Gradients", Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2006, pp. 1491-1498, IEEE Computer Society.

Cited By (27)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US8633999B2 (en) 2009-05-29 2014-01-21 DigitalOptics Corporation Europe Limited Methods and apparatuses for foreground, top-of-the-head separation from background
US8971628B2 (en) 2010-07-26 2015-03-03 Fotonation Limited Face detection using division-generated haar-like features for illumination invariance
US8977056B2 (en) 2010-07-26 2015-03-10 Fotonation Limited Face detection using division-generated Haar-like features for illumination invariance
US20120269428A1 (en) * 2011-04-25 2012-10-25 Daniel Bloom Mouth Corner Candidates
US8891876B2 (en) * 2011-04-25 2014-11-18 Hewlett-Packard Development Company, L.P. Mouth corner candidates
US10101636B2 (en) 2012-12-31 2018-10-16 Digitaloptics Corporation Auto-focus camera module with MEMS capacitance estimator
US11727426B2 (en) 2013-05-21 2023-08-15 Fotonation Limited Anonymizing facial expression data with a smart-cam
US9866748B2 (en) 2014-04-22 2018-01-09 Snap-Aid Patents Ltd. System and method for controlling a camera based on processing an image captured by other camera
US9661215B2 (en) 2014-04-22 2017-05-23 Snapaid Ltd. System and method for controlling a camera based on processing an image captured by other camera
EP4250738A2 (en) 2014-04-22 2023-09-27 Snap-Aid Patents Ltd. Method for controlling a camera based on processing an image captured by other camera
WO2015162605A2 (en) 2014-04-22 2015-10-29 Snapaid Ltd System and method for controlling a camera based on processing an image captured by other camera
US11019246B2 (en) 2015-04-27 2021-05-25 Snap-Aid Patents Ltd. Estimating and using relative head pose and camera field-of-view
US10594916B2 (en) 2015-04-27 2020-03-17 Snap-Aid Patents Ltd. Estimating and using relative head pose and camera field-of-view
US10419655B2 (en) 2015-04-27 2019-09-17 Snap-Aid Patents Ltd. Estimating and using relative head pose and camera field-of-view
US9754163B2 (en) 2015-06-22 2017-09-05 Photomyne Ltd. System and method for detecting objects in an image
US10198629B2 (en) 2015-06-22 2019-02-05 Photomyne Ltd. System and method for detecting objects in an image
US9928418B2 (en) 2015-06-22 2018-03-27 Photomyne Ltd. System and method for detecting objects in an image
US10452905B2 (en) 2015-06-22 2019-10-22 Photomyne Ltd. System and method for detecting objects in an image
US10136043B2 (en) 2015-08-07 2018-11-20 Google Llc Speech and computer vision-based control
US9769367B2 (en) 2015-08-07 2017-09-19 Google Inc. Speech and computer vision-based control
US10225511B1 (en) 2015-12-30 2019-03-05 Google Llc Low power framework for controlling image sensor mode in a mobile image capture device
US10728489B2 (en) 2015-12-30 2020-07-28 Google Llc Low power framework for controlling image sensor mode in a mobile image capture device
US10732809B2 (en) 2015-12-30 2020-08-04 Google Llc Systems and methods for selective retention and editing of images captured by mobile image capture device
US9836484B1 (en) 2015-12-30 2017-12-05 Google Llc Systems and methods that leverage deep learning to selectively store images at a mobile image capture device
US11159763B2 (en) 2015-12-30 2021-10-26 Google Llc Low power framework for controlling image sensor mode in a mobile image capture device
US9836819B1 (en) 2015-12-30 2017-12-05 Google Llc Systems and methods for selective retention and editing of images captured by mobile image capture device
US9838641B1 (en) 2015-12-30 2017-12-05 Google Llc Low power framework for processing, compressing, and transmitting images at a mobile image capture device

Also Published As

Publication number Publication date
US20070201725A1 (en) 2007-08-30
CN101427263B (en) 2013-08-07
US20110058058A1 (en) 2011-03-10
CN101427263A (en) 2009-05-06
US20110279700A1 (en) 2011-11-17
CN101427266B (en) 2012-10-03
US7804983B2 (en) 2010-09-28
US8005268B2 (en) 2011-08-23
CN101427266A (en) 2009-05-06

Similar Documents

Publication Publication Date Title
US8265348B2 (en) Digital image acquisition control and correction method and apparatus
US7995795B2 (en) Method and apparatus for selective disqualification of digital images
US7551754B2 (en) Method and apparatus for selective rejection of digital images
US10733472B2 (en) Image capture device with contemporaneous image correction mechanism
US9692964B2 (en) Modification of post-viewing parameters for digital images using image region or feature information
US8593542B2 (en) Foreground/background separation using reference images
US8682097B2 (en) Digital image enhancement with reference images
US8330831B2 (en) Method of gathering visual meta data using a reference image
US8675991B2 (en) Modification of post-viewing parameters for digital images using region or feature information
US9129381B2 (en) Modification of post-viewing parameters for digital images using image region or feature information

Legal Events

Date Code Title Description
AS Assignment

Owner name: FOTONATION VISION LIMITED, IRELAND

Free format text: ASSIGNMENT OF ASSIGNORS INTEREST;ASSIGNORS:STEINBERG, ERAN;CORCORAN, PETER;BIGIOI, PETRONEL;SIGNING DATES FROM 20060921 TO 20061003;REEL/FRAME:026653/0916

Owner name: TESSERA TECHNOLOGIES IRELAND LIMITED, IRELAND

Free format text: ASSIGNMENT OF ASSIGNORS INTEREST;ASSIGNOR:FOTONATION VISION LIMITED;REEL/FRAME:026653/0977

Effective date: 20101001

AS Assignment

Owner name: DIGITALOPTICS CORPORATION EUROPE LIMITED, IRELAND

Free format text: CHANGE OF NAME;ASSIGNOR:TESSERA TECHNOLOGIES IRELAND LIMITED;REEL/FRAME:027728/0339

Effective date: 20110713

STCF Information on status: patent grant

Free format text: PATENTED CASE

AS Assignment

Owner name: FOTONATION LIMITED, IRELAND

Free format text: CHANGE OF NAME;ASSIGNOR:DIGITALOPTICS CORPORATION EUROPE LIMITED;REEL/FRAME:034524/0693

Effective date: 20140609

FPAY Fee payment

Year of fee payment: 4

MAFP Maintenance fee payment

Free format text: PAYMENT OF MAINTENANCE FEE, 8TH YEAR, LARGE ENTITY (ORIGINAL EVENT CODE: M1552); ENTITY STATUS OF PATENT OWNER: LARGE ENTITY

Year of fee payment: 8

AS Assignment

Owner name: BANK OF AMERICA, N.A., AS COLLATERAL AGENT, NORTH CAROLINA

Free format text: SECURITY INTEREST;ASSIGNORS:ADEIA GUIDES INC.;ADEIA IMAGING LLC;ADEIA MEDIA HOLDINGS LLC;AND OTHERS;REEL/FRAME:063529/0272

Effective date: 20230501